PLoS ONEplosplosonePLOS ONE1932-6203Public Library of ScienceSan Francisco, CA USA10.1371/journal.pone.0231954PONE-D-19-22435Research ArticleBiology and life sciencesOrganismsEukaryotaAnimalsVertebratesAmniotesBirdsSeabirdsPenguinsBiology and life sciencesZoologyAnimalsVertebratesAmniotesBirdsSeabirdsPenguinsBiology and life sciencesAgricultureAquacultureFisheriesEarth sciencesAtmospheric scienceClimatologyClimate changePeople and placesGeographical locationsAntarcticaBiology and life sciencesOrganismsEukaryotaAnimalsVertebratesAmniotesMammalsMarine mammalsWhalesBiology and life sciencesZoologyAnimalsVertebratesAmniotesMammalsMarine mammalsWhalesBiology and life sciencesMarine biologyMarine mammalsWhalesEarth sciencesMarine and aquatic sciencesMarine biologyMarine mammalsWhalesBiology and life sciencesEcologyEcosystemsMarine ecosystemsEcology and environmental sciencesEcologyEcosystemsMarine ecosystemsBiology and life sciencesEcologyEcosystemsEcology and environmental sciencesEcologyEcosystemsEarth sciencesMarine and aquatic sciencesBodies of waterOceansAntarctic OceanComparing feedback and spatial approaches to advance ecosystem-based fisheries management in a changing AntarcticComparing feedback management and a marine protected area in the Antarctichttp://orcid.org/0000-0001-9514-7344KleinEmily S.ConceptualizationData curationFormal analysisFunding acquisitionInvestigationSoftwareValidationVisualizationWriting – original draftWriting – review & editing12¤*http://orcid.org/0000-0002-6989-1273WattersGeorge M.ConceptualizationData curationFunding acquisitionInvestigationMethodologyProject administrationResourcesSoftwareSupervisionVisualizationWriting – review & editing1Antarctic Ecosystem Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, La Jolla, California, United States of AmericaFarallon Institute, Petaluma, California, United States of AmericaRopert-CoudertYanEditorCentre National de la Recherche Scientifique, FRANCE
The authors have declared that no competing interests exist.
Current address: Frederick S. Pardee Center for the Study of the Longer-Range Future, Boston University, Boston, Massachusetts, United States of America
* E-mail: emily.klein04@gmail.com8920202020159e0231954882019442020This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
To implement ecosystem-based approaches to fisheries management, decision makers need insight on the potential costs and benefits of the policy options available to them. In the Southern Ocean, two such options for addressing trade-offs between krill-dependent predators and the krill fishery include “feedback management” (FBM) strategies and marine protected areas (MPAs); in theory, the first adjusts to change, while the latter is robust to change. We compared two possible FBM options to a proposed MPA in the Antarctic Peninsula and Scotia Sea given a changing climate. One of our feedback options, based on the density of Antarctic krill (Euphasia superba), projected modest increases in the abundances of some populations of krill predators, whereas outcomes from our second FBM option, based on changes in the abundances of penguins, were more mixed, with some areas projecting predator population declines. The MPA resulted in greater increases in some, but not all, predator populations than either feedback strategy. We conclude that these differing outcomes relate to the ways the options separate fishing and predator foraging, either by continually shifting the spatial distribution of fishing away from potentially vulnerable populations (FBM) or by permanently closing areas to fishing (the MPA). For the krill fishery, we show that total catches could be maintained using an FBM approach or slightly increased with the MPA, but the fishery would be forced to adjust fishing locations and sometimes fish in areas of relatively low krill density–both potentially significant costs. Our work demonstrates the potential to shift, rather than avoid, ecological risks and the likely costs of fishing, indicating trade-offs for decision makers to consider.
http://dx.doi.org/10.13039/100000875Pew Charitable Trusts31740http://orcid.org/0000-0001-9514-7344KleinEmily S.EK was supported by funding from the Pew Charitable Trusts, contract ID #31740. This funder had no role in study design, data collection and analysis, or preparation of the manuscript. Publication under peer review was a requirement of this funding source, but the funder did not take part in deciding where this manuscript would be submitted or any part of the submission process. There was no additional external funding received for this study.Data AvailabilityData are contained within the manuscript or are available in published papers cited therein, and all code and input files are available online at https://github.com/EmilyKlein/KPFM2.Introduction
Implementing an ecosystem approach to the management of fisheries, and balancing ecological and human needs, is widely acknowledged as critical (e.g. [1]), especially in a changing climate (e.g. [2]). A vital element of ecosystem-based approaches, across all variants (e.g. ecosystem-based management, ecosystem-based fisheries management, etc.), is dealing effectively with uncertainty and changing conditions (e.g. [2, 3]), especially given the potential impacts of climate change. How do we facilitate sustainable use of ecosystems while addressing an unclear future?
The Southern Ocean is an ecologically and economically rich region [4, 5] where the consequences of climate change, such as higher temperatures and declining sea-ice extent, are already being observed (e.g., [6–8]). Here, the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR or the Commission) is responsible for ensuring that the structure and function of marine ecosystems are maintained as humans utilize various ecosystem services [9, 10]. The Commission also recognizes the need to mitigate current and future climate-change effects (e.g., [11]). One approach the Commission has considered to meet its conservation objectives given variability and change is “feedback management” (FBM) of the Antarctic krill (Euphasia superba, hereafter krill) fishery [12, 13]. CCAMLR defines FBM for the Antarctic krill fishery as using “decision rules to adjust selected activities (distribution and level of krill catch and/or research) in response to the state of monitored indicators” [12].
Feedback management strategies have a long history of discussion within CCAMLR (e.g., [12, 14–17]) and in the wider literature (e.g., [18–20]). Here, we refer to FBM as CCAMLR defines it, wherein a decision rule is applied to adjust the catch levels and their spatial distribution based on the state of a monitored indicator. The decision rule itself is fixed and is not modified, but, since the prescribed response changes depending on the value of the indicator(s), FBM can react to changing conditions. The decision rule in FBM can also readily use indicators based on the status of, or changes in, non-target species, thus potentially allowing management to be more explicitly ecosystem-based [18]. Therefore, feedback management strategies are a possible approach by which decision makers can maintain an ecosystem focus while also addressing uncertainties around future climate change by regularly adjusting to changing conditions. We note that FBM is not adaptive management, as nothing is learned via the process and the decision rule is not updated [21], nor is it traditional fisheries management, as it does not require further information or modeling beyond the indicator [17, 22, 23].
Of course, FBM is only one tool in the proverbial toolkit; the effectiveness of such approaches needs to be assessed prior to implementation and against other options (e.g., [17, 18, 21]). Marine protected areas (MPAs), wherein extractive uses like fishing are limited or prohibited, are an alternative to FBM. Protected areas may also be valuable for addressing uncertainty [24–26] and applying an ecosystem approach to management (e.g. [27]). While FBM strategies adjust to change, effective and carefully designed MPAs are a strategy that can potentially be robust to change [28, 29].
Both FBM and MPAs have been prioritized for policy consideration in the Southern Ocean, and CCAMLR has established these priorities given the current and ongoing impacts of climate change and the need for management despite inevitable uncertainty. In 2011, the Commission adopted a set of objectives to be met by MPAs throughout the Southern Ocean (Conservation Measure 91–04) [30]. These objectives nominally parallel those of FBM as both ultimately intend to achieve the overarching aims of Article II of the Convention that established CCAMLR, namely the conservation of marine living resources [31–33]. The Commission is currently wrestling with the concept of “harmonizing” FBM with the establishment of an MPA in an important krill-fishing area in the Scotia Sea and Antarctic Peninsula region.
Both FBM and MPAs pose challenges in addition to their benefits, and both can be difficult to implement. To encourage their implementation, it is critical that the potential outcomes of these approaches are assessed, especially as the climate changes over the long term. Dynamic ecosystem models are useful tools to conduct such assessments [34] and may be especially valuable for evaluating the performance of MPAs [35]. These simulation models are increasingly used to support management decisions [36]. In the Southern Ocean, the Commission advocates using ecosystem models to expedite the delivery of scientific advice on FBM strategies [12], and such models are intended to and have been used to evaluate MPAs [37, 38].
Here, we utilized a dynamic ecosystem model [39] to support decision making in the Southern Ocean by comparing two FBM strategies against one another and against an MPA given the impact of climate warming on krill growth [40, 41]. For the two FBM strategies, we projected outcomes in which spatially resolved catch limits for “small scale management units” (SSMUs, [42]) were periodically updated based on indicators of either (1) krill density or (2) changes in penguin abundance. We also projected the outcomes of an MPA previously shown in other modeled scenarios to have ecological benefits and to be capable of buffering possible consequences of climate change [38]. Our comparison provides guidance on addressing an uncertain future with either FBM, an approach aimed at adjusting to change, or a potential MPA, which may be robust to change. Our findings are directly relevant to CCAMLR and active conversations therein, but also hold broad implications for ecosystem-based management in an uncertain future.
Methods
To evaluate two FBM strategies and a candidate MPA in the Antarctic Peninsula and Scotia Sea, we employed the Krill-Predator-Fishery Model (KPFM2, [39]). KPFM2 has previously been used to develop scientific advice on krill-fishery management (e.g., [38, 39, 41]). The model is minimally realistic, i.e. it focuses on the specific subset of a complex and coupled system that is deemed most relevant to the questions at hand (as in Plagányi et al. [43]). Here, as with previous use of KPFM2, those questions revolve around trade-offs between development and expansion of the international krill fishery and conservation of krill-dependent predators as they compete for finite krill resources.
Watters et al. [39] described KPFM2 in detail, with sensitivity analysis further provided by Hill & Matthews [44]. The model is currently parameterized with a seasonal time step (summer and winter) and projects outcomes for the fishery alongside those for four krill-dependent predator groups, penguins, seals, whales, and fish (Table 1). While both FBM strategies we address here involve a single indicator species, it is important to assess possible impacts on other species, which may also signal the potential for broader, system-level outcomes. We focus on penguins (an indicator in one strategy) and seals (not an indicator in either) to keep main text figures manageable, with results for whales and fish provided in (S1 File). We employed the ecological and model structure from Watters et al. [39], with the updates and MPA delineation outlined in [38]. Briefly, the dynamics of krill and each predator group are described by delay-difference equations (S1 File) in which temporal trends in abundance are recursive (e.g. the abundance of seals in one SSMU at the current time step depends on the abundance of seals in that same SSMU during the previous time step). Krill predators are modeled as resident populations in one SSMU (Table 1), with each population foraging across multiple SSMUs, as defined by recent tracking data collected during the breeding (summer) and non-breeding (winter) seasons. The post-larval biomass of krill in each SSMU is estimated at the beginning of each time step, and is determined by stochastic recruitment and area-specific mortality and movement. Competition arises when krill biomass is insufficient to satisfy the combined demand of predators and the fishery. Additional information is provided in the (S1 File), and input data and code are available open access and online [45]; however, such data and code repositories are not to serve as a comprehensive explanation of the ecosystem model and its use. For additional details on the model and its rationale, we strongly encourage interested readers to refer to Watters et al. [39].
10.1371/journal.pone.0231954.t001
Species composition of krill predator groups and where they are modeled as resident by small scale management unit (SSMU, Fig 1).
Predator group
Modeled as resident in SSMU
Common name (Species name)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Penguins
X
X
X
X
X
X
X
X
X
X
X
X
Adélie penguin (Pygoscelis adeliae)
gentoo penguin (Pygoscelis papua)
chinstrap penguin (Pygoscelis antarctica)
macaroni penguin (Eudyptes chrysolophus)
Seals
X
X
X
X
X
Antarctic fur seal (Arctocehpalus gazella)
Whales
X
X
fin whale (Balaenoptera physalus)
humpback whale (Megaptera novaeangeliae)
Minke whale (Balaenoptera bonaerensis)
southern right whale (Eubalaena australis)
blue whale (Balaenoptera musculus)
Sei whale (Balaenoptera borealis)
Fish
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Nichol’s lanternfish (Gymnoscopelus nicholsi)
Antarctic lanternfish (Electrona antarctica)
Macherel icefish (Champsocephalus gunnari)
Table adapted from Watters et al. [39].
We leveraged earlier implementations of KPFM2, including its “reference set” of four parameterizations that bracket key uncertainties about rates of area-specific krill movement between SSMUs (no movement and movement as passive drifters) and relationships between krill biomass and the effective numbers of breeding predators (hyperstable and linear) [39]. We used parameters values from previous implementations of KPFM2 (i.e. Watters et al. [39] with updates in [41, 38]), only adjusting for the FBM and the MPA scenarios considered here as described below. Spatially, the model arena covers three of the CCAMLR statistical subareas in the Atlantic Sector of the Southern Ocean, Subareas 48.1, 48.2, and 48.3 (Fig 1). These subareas are further subdivided into the SSMUs to better address the ecosystem impacts of krill fishing by providing a management mechanism to spatially distribute catches [42]. We report model outcomes aggregated across the model’s entire spatial arena (e.g. relative change in total number of seals in the entire arena) and at the SSMU scale (e.g. change in number of seals by SSMU).
10.1371/journal.pone.0231954.g001
Spatial structure of the ecosystem model.
Subareas 48.1, 48.2, and 48.3 are labelled, and within these, small-scale management units (SSMUs; [42]) are also outlined, as well as labeled in red; the modeled MPA is in light blue.
We modeled the krill fishery given a plausible future wherein the fishery is fully developed, i.e. the modeled fishery is allowed to take krill up to the total precautionary catch limit established by CCAMLR. Currently, catch limits for the krill fishery are much lower than those modeled here (about 0.01 times the biomass of krill in our study arena), however the Commission desires a spatial management strategy which successfully mitigates risks to krill predators and will ultimately allow catch limits to be increased and the fishery to fully develop [46]. Either FBM or an MPA could potentially constitute such a strategy, so we chose to model the fully developed fishery. Given this decision, we computed catch limits as the products of (1) the initial krill biomass across the model arena; (2) the harvest rate that CCAMLR used to establish the current total precautionary catch limit for krill in our study area (0.093); and (3) proportions that distribute the overall catch limit among SSMUs (and see Model Implementation of FBM, below). We set the proportions in (3) equal to the average spatial and seasonal distributions of catches taken by the krill fishery from 2009 to 2017 [47].
To consider climate change in our comparison of two FBM strategies and an MPA, we followed Klein et al. [41] and simulated the potential effect of changing water temperatures on the gross growth potential (GGP) of krill [40]. We adjusted the average mass of individual krill over 100 years using the same method as in our earlier work, but, to avoid doubling the number of scenarios modeled, only included trends in krill growth given temperature changes under the Representation Control Pathway (RCP) 8.5 [48]. This pathway assumes no future action is taken to mitigate climate change.
Model implementation of FBM
KPFM2 was designed to evaluate management strategies that adjust both the level and distribution of catch limits for krill, including simple FBM strategies. We updated and applied that latter functionality here–our only difference from the model implemented in previous work (e.g. [38, 39, 41]). To model how the overall catch limit is distributed among SSMUs under FBM, we developed two new “fishing options” in KPFM2 (sensu [49, 50]). For these FBM fishing options, we distributed overall catch limits based on SSMU-specific estimates of (1) the density of krill (g·m-2), hereafter “FBM-Krill”, or (2) changes in the abundance of breeding penguins, “FBM-Pengs”. The first option, FBM-Krill, is motivated by a recommendation from Hill et al. [51], and there has been substantial recent attention on endeavors to survey krill with fishing vessels (e.g., [52]). We developed FBM-Pengs based on the considerable interest in and development of efforts to estimate the abundance of breeding penguins from remotely sensed imagery as an indicator of ecosystem health (e.g., [53]). Therefore, of the myriad possible FBM approaches and indicators, such as changes in an environmental variable or abundance of other species in Antarctica, both of the FBM strategies simulated here have interest and support among a number of Southern Ocean stakeholders.
We executed the FBM fishing options using the existing reassessment framework in KPFM2 [49, 54]. In this framework, the distribution of catch limits among SSMUs is reassessed and adjusted at regular intervals during model simulations [49]. For such reassessments, the model “samples” previously simulated data at user-specified intervals and updates the catch-limit distribution as needed. We redistributed catch limits among SSMUs via this reassessment every five years. In FBM-Krill, the proportional distribution of the overall catch limit was based on the relative distribution of krill density among SSMUS; SSMUs with the highest density of krill were allocated the highest catch limits. For FBM-Pengs, the proportional distribution of the overall catch limit was based on changes in the abundance of breeding penguins, with the highest catch limits allocated to the SSMU with the greatest increase (or smallest decrease) in penguin abundance. Also in FBM-Pengs, the SSMU with the greatest decline in abundance during the reassessment interval was closed to fishing during the following interval.
In KPFM2, the overall catch limit is proportionally distributed among SSMUs following equation A.8 in Watters et al. [39];
Θi=(B0*γ*p′i)wi¯
where Θi is the catch limit allocated to SSMU i, B0 the initial biomass of krill, γ the harvest rate used to compute the overall catch limit from the initial biomass, p′i the proportional distribution of the overall catch limit to SSMU i (also called an “allocation fraction”), and wi¯ is the average mass (g) of krill in SSMU i. We used p′i to regularly update how the overall catch limit was distributed among SSMUs via the FBM fishing options by adjusting p′i in response to monitored indicators, either krill density (g·m-2) for FBM-Krill or changes in the abundance of breeding penguin for FBM-Pengs.
With FBM-Krill, we “sampled” krill density, di, during the summer season in a reassessment year (timestep t).
di=Ki,tAiKi,t is the abundance of krill in SSMU i at time t, and Ai is the area of SSMU i. We rescaled these density estimates to occur in the interval [0.0, 1.0] for use as p′i.
p′i=di∑di
For FBM-Pengs, we sampled the abundance of breeding penguins in each SSMU during summer of a reassessment year, time t, and compared it with abundance during the summer in the previous assessment year, time t-10 (KPFM2 is currently parameterized with two seasons per year and we reassessed the status of our indicators every five years). We then computed the difference between these samples (ΔPi);
ΔPi=Pi,t−Pi,t−10Pi,t is the abundance of penguins in SSMU i and at time t. As changes in abundance can be positive or negative, we remapped the SSMU-specific changes in abundance to be ≥ 0;
pi=ΔPi+(α×|min(ΔP)|)
ΔP is the vector of changes in penguin abundance that includes all SSMUs, and α is a scalar that determines the sensitivity of allocation fractions to changes in penguin abundance. Here, we set α to 1.0, which also closed the SSMU with the greatest decline in penguin abundance to fishing until the next reassessment. Finally, we rescaled each pi to occur in the interval [0.0, 1.0];
p′i=pi∑pi
Model implementation of the MPA
To implement an MPA in KPFM2, we used boundaries previously proposed to CCAMLR by the Delegations of Argentina and Chile (Fig 1) [55, 56]. The MPA is confined to Planning Domain 1, i.e. Subareas 48.1 and 48.2, and is therefore referred to here and by CCAMLR as the Domain 1 MPA, or “D1MPA” [57]. An MPA already exists in Planning Domain 1, the South Orkney Islands Southern Shelf MPA [58], and we combined this with the D1MPA for our analysis. For simplicity, all areas within the MPA are treated as “no-take” areas in KPFM2, i.e. they are closed to fishing, and all areas outside the MPA are open to fishing. We implemented the MPA and relevant model parameters as in [38], but here we included the effects of warming temperatures on krill growth (from [41] as discussed above).
We previously modeled three alternative reallocations of fishing displaced by an MPA [38], but, for simplicity, only include one here. We redistributed displaced catches across all open areas in the model arena, in proportion to the recent spatial and seasonal distribution of krill catches by SSMU (2009–2017, [47]). Therefore, while it is difficult to predict how catches will reallocate in reality, the alternative used here is informed by recent, real-world fishing patterns. Also, in the model, this distribution previously projected the greatest benefits (highest catches) and lowest costs (lowest probability of fishing in areas of low krill density) for the fishery in the model [38].
Scenario assessment
We ran five scenarios using the KPFM2 model: FBM-Krill, FBM-Pengs, and the MPA, as well as “No FBM” and “No MPA” ‘base case’ scenarios. The base case scenarios were parameterized identically to their analogous FBM or MPA scenarios except they did not include the management strategy, i.e. FBM or an MPA. For all scenarios, we projected outcomes to the end of the 21st century and ran 1001 Monte Carlo trials (with random variations in krill recruitment) of each scenario and across all four parameterizations noted above (and described in Watters et al. [39]; i.e., we ran 4004 Monte Carlo trials per scenario). As with previous implementation of KPFM2, we averaged results across trials and parameterizations for each scenario to account for model uncertainty (see [39, 41]). A schematic of this process is provided in Fig 2.
10.1371/journal.pone.0231954.g002
Schematic of the modelling process.
For both the ‘base case’ (left column, top grey boxes) and FBM or MPA (right column, top blue boxes) scenarios, the model is run across all four parameterizations (top boxes) and across 1001 Monte Carlo trials. These are then averaged for final results (green circle), and the relative change assessed (bottom row of boxes).
We computed outcomes in terms of predator abundance and catches taken by the fishery at two points in time to assess outcomes after 30 years and at the end of the run. We also considered results aggregated across the entire model spatial arena, for broad overall changes, and at the SSMU scale, for spatial differences. To make outcomes comparable across the differing management strategies, we report results relative to the respective No FBM or No MPA base case scenario (e.g., the ratio of catch under FBM to catch in the No FBM reference; a ‘counterfactual’ as described in [35], Fig 2). Using this approach, results equal 1.0 if the management action, FBM or the MPA, did not affect outcomes, while results >1.0 and <1.0 respectively indicate increases (positive outcomes) and decreases (negative outcomes). As an additional metric of fishery performance, we also computed the SSMU-specific probabilities that the fishery would suspend operations because krill density fell below 15 g·m-2, a level previously identified as an important threshold for the fishery [51]. The risks of incurring such “threshold violations” indicate whether redistributing catches may increase the costs of fishing beyond those related to changing catches themselves.
To aid in comparing results, we also determined an arbitrary scale: an absolute change of 0.01 to 0.10 denotes a “small” change (either increase or decrease), > 0.10 to 0.50 a “medium” change, and > 0.50 a “large” change. We stress that this scale is simply for comparison, and we do not attribute significance to these ranges. The significance of a change in abundance will depend on the species, and that of a change for the fishery will be interpreted differently by different people. Further, we note that our results are model projections and should be taken as strategic advice, meaning readers should consider overall patterns not specific numbers.
Results
Relative abundances of krill predators were sensitive to the management strategies investigated here. For results aggregated across the model arena, FBM-Krill caused little change in the total abundances of penguins or seals relative to the No FBM scenario (“small” or no absolute changes, all < ±0.10%), but more obvious declines were projected for both species groups with FBM-Pengs (Fig 3A), with a small decline (-0.04) in penguins and a medium decline (-0.11) in seals. Projected outcomes were similar after 30 and 100 years for FBM-Krill, but total declines under FBM-Pengs became greater over time (-0.09 for penguins and -0.30 for seals). In contrast, the MPA was projected to provide small increases in penguin abundance relative to the base case scenario without an MPA (+0.09), but with medium increases (+0.35) by the end of the model century. The relative abundance of seals declined with the MPA, but this decrease was small (-0.05) and did not change over time in the model runs. Whales saw small increases under FBM-Pengs (+0.02), with larger but still small increases under the MPA (+0.05), and both increases were greater over time (+0.04 and +0.09). No changes were found for whales with FBM-Krill or for the fish group under any of the scenarios (all absolute changes < 0.01) (S1 Fig).
10.1371/journal.pone.0231954.g003
Relative total change in predator abundance (A) and fishery performance (B and C) across modeled scenarios and aggregated at the scale of the full model arena. Lighter shades are at 30 years in the model run, and darker shades at 100 years. Fishery performance is measured both as relative catch (B) and the probability of a threshold violation (C). Feedback strategies are indicated in blue, and the MPA in orange. All results are relative to the No FBM or No MPA scenarios, with the dashed grey line at 1.0 indicating no impact of FBM or the MPA.
In general, we projected only small changes in fishery performance. For the krill fishery, projected catches over the whole model arena were relatively unaffected by the two FBM strategies and the MPA (Fig 3B). FBM-Krill resulted in small or no change (-0.01 and +0.005 for FBM-Krill and -0.01 and +0.03 for FBM-Pengs, for 30 and 100 years, respectively), and the MPA only a small increase that did not change over time (+0.04). We found comparatively greater differences between management strategies in terms of the probabilities of threshold violations, with small changes under FBM-Krill (-0.01 across time in the model), slightly larger but still small decrease with FBM-Pengs (-0.03 at 30 years and -0.05 at the end of the model run), and medium increases with the MPA (+0.15 and +0.11) (Fig 3C).
At the smaller SSMU spatial scale, our model projected the relative abundances of krill predators to vary spatially and across scenarios (due to the large number of results, we have projected outcomes as maps, with results available in the S1 Data). FBM-Krill generally provided slight benefits to penguins and seals in several SSMUs (Fig 4A–4D), but outcomes from FBM-Pengs were more mixed, with relative abundances increasing in some SSMUs and decreasing in others (Fig 5A–5D). While there were only slight difference in SSMU-specific outcomes from FBM-Krill after 30 and 100 years (Fig 4A–4D), we projected that FBM-Pengs intensified changes in predator abundance over time (Fig 5A–5D). With the MPA, variability in the relative abundances of penguins and seals among SSMUs was intermediate to the levels of variability under FBM-Krill and FBM-Pengs, but with greater positive outcomes projected across most SSMUs for penguins (Fig 6A–6D). These results were similar for whales and fish (slight increases in some areas with FBM-Krill, variable outcomes in FBM-Pengs and the MPA), and the SSMU-specific outcomes for these groups generally changed little over time (S2–S4 Figs).
10.1371/journal.pone.0231954.g004
SSMU-specific outcomes of FBM-Krill for predators and the fishery under a modeled climate change impact.
Projected penguin (A, B) and seal (C, D) abundances, and krill catches (E, F) given climate-change impacts on krill growth, with outcomes at 30 years in to the model run in the left column (A, C, E) and at 100 years in the right (B, D, F). Blues represent increases relative to the No FBM base case scenario and reds decreases; white and light colors indicate no or little change. Light grey denotes areas where the species group is not modeled as resident. Note changes are relative to the No FBM base case within each SSMU, not over the entire model arena.
10.1371/journal.pone.0231954.g005
SSMU-specific outcomes of FBM-Pengs for predators and the fishery under a modeled climate change impact.
Projected penguin (A, B) and seal (C, D) abundances, and krill catches (E, F) given climate-change impacts on krill growth, with outcomes at 30 years in the left column (A, C, E), and 100 years in the right (B, D, F). All other details as in Fig 4.
10.1371/journal.pone.0231954.g006
SSMU-specific outcomes of the MPA for predators and the fishery under a modeled climate change impact.
Projected penguin (A, B) and seal (C, D) abundances, and krill catches (E, F) given climate-change impacts on krill growth, with outcomes at 30 years in to the model run in the left column (A, C, E), and at 100 years in the right (B, D, F). All other details as in Fig 4, aside from the base case being the No MPA scenario.
We projected performance of the krill fishery would also vary by SSMU. FBM-Krill resulted in various changes in relative catch, with decreases in most coastal SSMUs in Subarea 48.1 and 48.2, and increases elsewhere (Fig 4E–4F). FBM-Pengs projected even more pronounced changes in relative catch, but, again, decreases were projected in some coastal SSMUS and increases in others (Fig 5E and 5F). We found comparatively smaller changes in relative catch with the MPA, with catches increasing in many SSMUs and declines in only one (Fig 6E and 6F).
We also explored whether changes in relative abundance of krill-dependent predators were related to changes in relative catch. Fig 7 compares relative change in catch with either FBM or the MPA against relative change in penguin (Fig 7A and 7B) or seal (Fig 7C and 7D) abundance, with points to the right of the dashed vertical x = 1 line denoting greater catch with FBM or the MPA, and points above the horizontal y = 1 indicating increases in penguin or seal abundance. For penguins, several patterns emerge (Fig 7A and 7B). First, the majority of the blue points (either squares, FBM-Krill, or triangles, FBM-Pengs) are left of the x = 1 line, indicating relatively lower catches with FBM in these SSMUs, while the orange circles denoting the MPA are almost all to the right of this line, indicating relatively greater catches with the MPA. That is, both FBM-Krill and FBM-Pengs result in more SSMUs with less catch, whereas more SSMUs yield relatively greater catches with the MPA. Second, all SSMUs under either FBM strategy where penguins increased in the model (blue squares and triangles above the y = 1 line) were also in SSMUs with lower catches. In contrast, both catches and penguin abundances increased in most SSMUs with the MPA (orange circles are to the right of the x = 1 line and above the y = 1 line). These patterns somewhat held for seals, except that the MPA did not increase seal abundance (i.e. orange circle are generally not above the y = 1 line in Fig 7C and 7D), a result also noted in [38]. Finally, a third pattern emerged for penguins that was not apparent for seals: in SSMUs where penguins did increase and catches were lowest, FBM-Pengs did project greater increases in penguins than FBM-Krill. These patterns were conserved over the course of the model run, with slight increases in abundance for penguins (outcomes for whales and fish in S5 Fig).
10.1371/journal.pone.0231954.g007
Relationship between relative catches and the relative abundances of penguins and seals given the two FBM strategies and an MPA and a modeled impact of climate change.
Relative catches (FBM/No FBM or MPA/No MPA, x-axis) and relative changes in the abundances (FBM/No FBM or MPA/No MPA, y-axis) of penguins (A, B) and seals (C, D) given FBM-Krill (light blue squares), FBM-Pengs (dark blue triangles), and the MPA (orange circles) at 30 years (left column, A and C) and at 100 years (right column, B and D). The dashed lines represent no change with FBM or the MPA in catch or abundance at x = 1 and y = 1, respectively.
Discussion
The management strategies considered here, if implemented, seem likely to land decision makers at different locations within the multivariate tradeoff space inherent in managing the krill fishery. We found that adjusting to future change by monitoring krill density (FBM-Krill) offered slight benefits to some predator populations while simultaneously maintaining catches taken by the fishery and mitigating the risk that the fishery would be displaced to areas of low krill density. In contrast, when adjustments to future change were based on the abundances of breeding penguins (FBM-Pengs), we projected decreases in the relative abundances of some penguin populations and substantially more spatial variability in the relative abundances of all krill predators alongside only minor increases in relative catches and a lower probability of threshold violations, i.e., fishing in areas of low krill density. When we simulated an MPA scenario, almost all penguin populations and catches across most SSMUs increased, but we also projected declines in the relative abundance of seals and an increased probability of threshold violations.
The tradeoffs described above have taught us at least three useful and generalizable lessons. First, it seems easy to design a well-intentioned but undesirable FBM strategy. We believe that FBM-Pengs represents one such strategy. Although FBM-Pengs was designed to adjust to future change by observing penguins, this approach was the worst at conserving krill predators, including penguins, of the three management strategies we considered. We discuss why this might be the case later in our Discussion. Second, it seems that FBM strategies which are explicit about prey but not their predators may nevertheless be useful for conserving predators while being adjustable in a changing future. While it did not deliver large benefits in the model, we believe FBM-Krill still demonstrates this potential. FBM-Krill did not exacerbate declines in the relative abundances of krill-dependent predators and some populations increased, even with the fishery fully developed, identifying a potential strategy for adjusting to change while maintaining predator populations and the fishery. Our final lesson is that management strategies designed to be robust to future change, like the MPA considered here, may provide ecological benefits, but may also involve strong tradeoffs between resource conservation and utilization.
Alongside previous work showing that fishing for krill can increase risks to predators (e.g., [41, 42]), our results continue to indicate that benefits for predators may accrue in locations where forage-fish catches are reduced. Both FBM scenarios considered here, but more strongly FBM-Pengs, showed increases in the relative abundances of predators in SSMUs where relative catches decreased (Fig 7). A different pattern emerged for the MPA. The SSMUs with increased predator abundances, particularly of penguins, were also projected to support comparatively larger catches of krill. We suggest that this result arises from how displaced catches were spatially reallocated in the model. Catches could still be taken from SSMUs in which fishing recently occurred but which were not fully encompassed by the MPA. Therefore, displacement was minimal relative to the stricter redistribution in the FBM-Pengs scenario. Collectively, our results demonstrate an underlying difference in how FBM and MPA strategies may provide ecological benefits: by shifting the location of fishing due to changes in an indicator and possibly to completely new areas (FBM), or away from critical areas altogether but allowing for effort to continue nearby (MPA).
As noted previously, our findings also indicate that shifting the spatial distribution of catches with FBM strategies may not always reduce ecological risk as intended. The FBM-Pengs scenario failed to provide broad benefits for krill predators–even for the indicator species, penguins–and, instead, caused some populations to decline. We believe this result is due to our implementation of FBM-Pengs. We conserved the overall catch limit–that is, the fishery harvested the same amount of krill—and spatially allocated catch limits based on SSMU-specific changes in penguin abundance relative to overall changes in penguin abundance throughout the model arena. Those SSMUs with the greatest declines in penguin abundance saw the greatest reductions in catch, but catch allocations to SSMUs with relatively smaller penguin declines may not have decreased and may even have increased if there were larger penguin declines elsewhere. That is, our modeled FBM strategy successfully displaced catches, but did not always shift catches away from vulnerable populations. In some cases, catches were displaced into SSMUs where the abundance of predators was indeed declining.
We do note that the SSMUs with the greatest reductions in catch did see strong positive outcomes for many krill-dependent predators, particularly penguins, indicating that FBM-Pengs achieved ecological goals for some populations in the model (Fig 7). Given this, an alternative formulation of FBM-Pengs might be to reduce SSMU-specific catch limits based on changes in penguin abundance in that SSMU regardless of such changes in other areas. This alternative would ensure decreased catches with decreases in penguin abundance—but might also reduce the overall catch when penguin declines are widespread. We conserved the overall catch limit because we assumed such an approach would be more desirable to some CCAMLR Members and the fishing industry. However, if region-wide declines in penguin abundance continue as a function of climate change, increasing cetacean populations, and other drivers (e.g., [7,59–62]), our results imply that overall reductions in catches may be required by an FBM strategy that aims to mitigate declines in predator abundance (e.g., as discussed for CCAMLR in [9]). More broadly, the outcomes from FBM-Pengs demonstrate the significance of carefully considering the indicators and decision rules used within FBM strategies; decision rules that are explicit about predators will not necessarily ensure that the objectives of ecosystem-based fisheries management are achieved.
It is tempting to believe the routine application of a preset decision rule (e.g., [21]) is beneficial. In our experience, preset decision rules can efficiently increase transparency and the use of “best available science” in fisheries management. However, our results show that when preset decision rules are poorly specified, as with FBM-Pengs, negative consequences can be exacerbated over the course of time (e.g., Fig 5). Management strategies that fail to conserve predator populations in the near term seem likely fail over the long term as well, even if these strategies are designed to adjust to future change. Yet our projections with FBM-Krill and the MPA also indicate that management strategies which are successful in the near term may help to mitigate negative outcomes in the long run as well (Figs 3, 4, 6 and 7).
Our work also indicates that mandated shifts in the spatial distribution of fishing required by an FBM strategy or an MPA may be burdensome to the fishing industry, and redistributing fishing effort can come with significant costs [63–65]. All the scenarios we explored here would displace fishing from the coastal SSMUs surrounding the Antarctic Peninsula and islands in Subareas 48.1, 48.2, and 48.3. Watters et al. [39] demonstrated redistributing fishing farther offshore, to “pelagic SSMUs”, could increase the ability of CCAMLR to achieve its conservation objectives, but these offshore areas have lower krill densities than coastal areas. Consequently, all three of the scenarios we explored here would, if implemented, be likely to shift fishing offshore but also decrease performance of the fishery. In addition, the redistribution of effort necessitated by an FBM strategy, including that to pelagic areas with lower krill densities, would be required and change after each assessment. In contrast, an MPA would close areas outright, and, after an MPA is established, the redistribution of fishing activity would likely be determined by the fleet itself. Indeed, previous results suggest that an effective MPA could allow fishing vessels to self-select grounds in open areas, without a need to further distribute catches spatially [38]. Thus, a primary difference between the FBM and MPA scenarios we considered is the degree of prescription about where fishing may occur. Such differences, as well as the spatial expression of possible costs, are critical for decision makers to consider in light of stakeholder needs and preferences.
Of course, reality is more complicated than our model is able to reflect, and modeling of such complex systems comes with caveats and assumptions. The caveats associated with KPFM2 are well defined in the literature [38, 39, 41, 43, 44]. These caveats include using aggregated predator groups and specifying certain functional relationships. Using aggregate groups may mean we are missing important dynamics for certain species within those groups, and alternative functional relationships may be more appropriate in some cases or in the future. We also make assumptions about krill densities and the spatial distribution of krill, as well as fishing patterns and the redistribution of fishing effort displaced by the MPA, the latter of which also has implications for fishery performance [38]. Moreover, for simplicity, we simulated climate-change effects of krill GGP using only RCP 8.5 [48], which assumes no action is taken on climate change. Therefore, on one hand, the results here could be seen as a “worst case” scenario. However, on the other hand, we implemented only one potential consequence of climate change on krill alone. In reality the effects of climate change will be more complicated than we have simulated. These effects may make populations more or less vulnerable, exacerbating or mitigating the outcomes projected here (as discussed in [41]). Despite all the caveats of our work, we assert that the broad implications of our results remain useful.
There are also a plethora of potential FBM and MPA scenarios that could be modeled. Here, both were based on community feedback and interest (FBM), and proposals based on community engagement (the D1MPA and the South Orkney Islands Southern Shelf MPA, [55, 56, 58]). Our previous research showed the D1MPA could be improved to better achieve various ecological outcomes [38]. Improved FBM scenarios are equally possible, and the challenges with FBM found here might be overcome by identifying other strategies that more readily provide region-wide benefits. Our goal was not to find a “best” scenario, but rather to compare possible outcomes and thereby supply strategic guidance regarding FBM strategies and MPAs given a changing climate. We contend the strategies considered here provide insight that is useful regardless of whether there are better or more preferable alternatives.
Our work evokes an interest in contrasting management strategies that dynamically adjust to change (e.g., FBM) against those that are statically robust to change (e.g., MPAs). Neither type of strategy is a panacea, and we have identified a potentially pathological FBM strategy (FBM-Pengs), while other research demonstrates concerns with MPAs (e.g., [63, 66]). Nevertheless, both FBM and MPAs may offer significant benefits, and we do not think contrasts between dynamic and static management strategies can easily be generalized. Some FBM strategies may require fewer data and analyses than more traditional approaches [17]; limit “haggling” that slows the management process [21]; are more tenable to the fishing industry due to the continuity of the approach [19]; stabilize harvested systems and avoid fishery collapse [22, 26]; and meet multiple management objectives [18]. We reason all of these benefits can also be the outcomes of a well-designed MPA. Conversely, we contend that the benefits of an MPA can be the outcomes of a well-designed FBM strategy, including reducing or reversing adverse human impacts [27, 67–69], buffering against uncertainty [24, 25], providing ecological benefits [70–72], and improving fishery yields [73–75].
We emphasize that FBM strategies and MPAs necessitate continued engagement in the management process once an approach is implemented, but the nature of that engagement warrants consideration. By definition, feedback strategies require monitoring of indicators; an MPA obliges monitoring to assess the effectiveness of the protected area. That is, monitoring to support an FBM strategy is, at a minimum, determined by what is needed to implement the decision rule, whereas monitoring to support an MPA may be more flexible and left to the discretion of the research community to determine whether the protected area is successful. These requirements should also be considered when making decisions.
Ultimately, the implementation of either an FBM strategy or an MPA will depend on the values of stakeholders involved as well as practical and political realities. Work such as ours aims to facilitate discussion by providing insight on potential benefits and costs of alternative management strategies, supporting CCAMLR’s existing management frameworks that endeavors to establish FBM and/or MPAs, and exemplify actionable science for policy [76]. This is particularly important as either strategy will require time and resources to implement and maintain, and CCAMLR is currently working to harmonize the D1MPA with FBM because the proposed MPA encompasses an important krill fishing area. Other researchers have also discussed the value of combining approaches, suggesting the use of either FBM [26] or adaptive management [77] to improve MPAs. One possibility might be to implement the D1MPA and later implement an FBM or more fully adaptive strategy that aims to adjust the MPA over time. In fact, an early version of the D1MPA proposal before CCAMLR [78] takes a step towards adaptive management. That proposal includes sequentially closing areas to assess the utility of an MPA and incorporating special zones in which experimental krill fishing could be conducted with the purpose of parameterizing one or more decision rules for an FBM strategy.
Strategic advice is crucial for decision makers looking to advance ecosystem-based management, including approaches such as FBM and MPAs, and will likely prove useful in a changing, uncertain future. Advice that compares potential strategies allows for more informed decision making and advances towards fully realized ecosystem-based adaptive management, and our work supports colleagues who have asserted that ecosystem models are valuable for providing such advice [34–36] and current practices in CCAMLR [10, 76]. Stakeholders in the Southern Ocean vary on what features of FBM and MPAs are most appealing and acceptable [79], but, critically, CCAMLR’s participatory process allows for broad consideration of possible options and their outcomes. This process also provides a framework to collectively determine the best way forward.
Supporting information
Relative changes in the abundance of additional predator groups across modeled scenarios and aggregated at the scale of the full model arena.
Lighter shades are at 30 years in the model run, and darker shades at 100 years. Feedback strategies are indicated in blue, and the MPA in orange. All results are referenced to the No FBM or No MPA scenarios, with the dashed grey line at 1.0 indicating no impact of FBM or the MPA.
(TIF)
SSMU-specific outcomes of FBM-Krill for additional predator groups under a modeled climate change impact.
Projected whale (A, B) and fish (C, D) abundances given climate-change impacts on krill growth, with outcomes at 30 years in to the model run in the left column (A, C) and at 100 years in the right (B, D). Blues represent increases relative to the No FBM base case scenario and reds decreases; white and light colors indicate no or little change. Light grey denotes areas where the species group is not modeled. Note changes are relative to the No FBM base case within each SSMU, not the entire model arena.
(TIF)
SSMU-specific outcomes of FBM-Pengs for additional predator groups under a modeled climate change impact.
Projected whale (A, B) and fish (C, D) abundances given climate-change impacts on krill growth, with outcomes at 30 years in to the model run in the left column (A, C), and at 100 years in the right (B, D). All other details as in S2 Fig.
(TIF)
SSMU-specific outcomes of the MPA for additional predator groups under a modeled climate change impact.
Projected whale (A, B) and fish (C, D) abundances given climate-change impacts on krill growth, with outcomes at 30 years in to the model run in the left column (A, C), and at 100 years in the right (B, D). All other details as in S2 Fig, aside from the base case being the No MPA scenario.
(TIF)
Relationship between relative catches and the relative abundances of whales and fish given the two FBM strategies and an MPA and a modeled impact of climate change.
Relative catches (FBM/No FBM or MPA/No MPA, x-axis) and relative changes in the abundances (FBM/No FBM or MPA/No MPA, y-axis) of whales (A, B) and fish (C, D) given FBM-Krill (light blue squares), FBM-Pengs (dark blue triangles), and the MPA (orange circle) at 30 years (left column, A and C) and at 100 years (right column, B and D). The dashed lines represent no change in catch or abundance at x = 1 and y = 1, respectively.
(TIF)
(DOCX)
(XLSX)
We thank M. Santos (Instituto Antártico Argentino), A. Capurro (Dirección Nacional del Antártico, Argentina), and C. Cárdenas (Instituto Antártico Chileno) for graciously granting permission to cite working papers and shapefiles that describe the D1MPA. We also thank A. Dahood for helping to create Fig 1.
ReferencesPikitchEK, SantoraC, BabcockEA, BakunA, BonfilR, ConoverDOet al. Eocsystem-based fishery management. . 2004; 305:346–347. doi: 10.1126/science.109822215256658MarascoRJ, GoodmanD, GrimesCB, LawsonPW, PuntAE, QuinnTJ. Ecosystem-based fisheries management: some practical suggestions. . 2007;64:929–939.RosenbergAA, McLeodKLImplementing ecosystem-based approaches to management for the conservation of ecosystem services. . 2008; 300:241–296.GrantSM, HillSL, TrathanPN, MurphyEJ. Ecosystem services of the Southern Ocean: trade-offs in decision-making. . 2013;25(5):603–17. doi: 10.1017/S095410201300030824163501NicolS, FosterJ, KawaguchiS. The fishery for Antarctic krill–recent developments. . 2012;13(1):30–40.MeredithMP, KingJC. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. . 200532(19).TrivelpieceWZ, HinkeJT, MillerAK, ReissCS, TrivelpieceSG, WattersGM. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. . 2011;108(18):7625–8. doi: 10.1073/pnas.101656010821482793FloresH, AtkinsonA, KawaguchiS, KrafftBA, MilinevskyG, NicolS, et al. Impact of climate change on Antarctic krill. . 2012;458:1–19.ConstableAJ. Lessons from CCAMLR on the implementation of the ecosystem approach to managing fisheries. . 2011;12(2):138–51.ConstableAJ, de la MareWK, AgnewDJ, EversonI, MillerD. Managing fisheries to conserve the Antarctic marine ecosystem: practical implementation of the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR). . 2000;57(3):778–91.Constable, AJ. SC-CAMLR work on Climate Change (paper XP19 to CEP–SC-CAMLR Workshop 2016). 2016. Report No.: WG-EMM-16/71.Scientific Committee for the Conservation of Antarctic Marine Living Resources (SC-CAMLR). Report of the Thirtieth Meeting of the Scientific Committee. 2011. Report No.: SC-CAMLR-XXX.Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Report of the Thirty-Second meeting of the Commission. 2013. Report No.: CCAMLR-XXXII. Available from https://www.ccamlr.org/en/meetings/26.Watters G, Hinke JT. Feedback management pro forma based on WG-EMM 12/44. 2015. Report No.: WG-EMM-15/33.Reiss CS, Watters G, Hinke JT, Kinzey D. Within season feedback management system–a pro forma for discussion at WG-EMM 2015. 2015. Report No.: WG-EMM-15/04.Watters G, Hinke JT, Reiss C. A feedback management strategy for the krill fishery in Subarea 48.1. 2016. Report No.: WG-EMM-16/48.Plagányi ÉE, Butterworth DS. An Illustrative Management Procedure for exploring dynamic feedback in krill catch limit allocations among small-scale management units. 2006. Report No.: WG-EMM-06/28.HillSL, CannonM. A potential feedback approach to ecolsystem-based management: Model predictive control of the Antarctic krill fishery. . 2013;20:119–37.TanakaS. A Theoretical Consideration on the Management of a Stock-Fishery System by Catch Quota and on Its Dynamical Properties. . 1980;46(12):1477–82.KaiM, ShirakiharaK. A feedback management procedure based on controlling the size of marine protected areas. . 2005;71:56–62.ButterworthDS. Why a management procedure approach? Some positives and negatives. . 2007;64(4):613–7.HaradaY, SakuramotoK, TanakaS. On the stability of the stock-harvesting system controlled by a feedback management procedure. . 1992;34:185–201.WaltersCJ, CollieJS. Is Research on Environmental Factors Useful to Fisheries Management? . 1988;45(10):1848–54.AllisonGW, GainesSD, LubchencoJ, PossinghamHP. Ensuring persistence of marine reserves: catastrophes require adopting an insurance factor. . 2003;13(1):S8–S24.GraftonRQ, KompasT, LindenmayerD. Marine reserves with ecological uncertainty. . 2005;67(5):957–71. doi: 10.1016/j.bulm.2004.11.00615998490KaiM, ShirakiharaK. Effectiveness of a feedback management procedure based on controlling the size of marine protected areas through catch per unit effort. . 2008;65(7):1216–26.RobertsCM, HawkinsJP, GellFR. The role of marine reserves in achieving sustainable fisheries. . 2005;360(1453):123–32.McLeodE, SalmR, GreenA, AlmanyJ. Designing marine protected area networks to address the impacts of climate change. . 2009;7(7):362–70.MicheliF, Saenz-ArroyoA, GreenleyA, VazquezL, Espinoza MontesJA, RossettoM, et al. Evidence that marine reserves enhance resilience to climatic impacts. . 2012;7(7):e40832. doi: 10.1371/journal.pone.004083222855690Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) Conservation Measure 91–04: General framework for the establishment of CCAMLR Marine Protected Areas. 2011. Available at: https://www.ccamlr.org/en/measure-91-04-2011.Convention on the Conservation of Marine Living Resources (CAMLR Convention). 1982. Canberra, Australia, 7–20 May, 1980. Available from: https://www.ccamlr.org/en/organisation/camlr-convention-text.Scientific Committee for the Conservation of Antarctic Marine Living Resources (SC-CAMLR). Report of the Twenty-Fourth Meeting of the Scientific Committee. 2005. Report No.: SC-CAMLR XXIV.BrooksCM, CrowderLB, CurranLM, DunbarRB, AinleyDG, DoddsKJ, et al. Science-based management in decline in the Southern Ocean. . 2016;354(6309):185–7. doi: 10.1126/science.aah411927738163LinkJS, IhdeTF, HarveyCJ, GaichasSK, FieldJC, BrodziakJKT, et al. Dealing with uncertainty in ecosystem models: The paradox of use for living marine resource management. . 2012;102:102–14.FultonEA, BaxNJ, BustamanteRH, DambacherJM, DichmontC, DunstanPK, et al. Modelling marine protected areas: insights and hurdles. . 2015;370(1681).TittensorDP, EddyTD, LotzeHK, GalbraithED, CheungWWL, BarangeM, et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. . 2018;1442:1421–42.DahoodA., WattersG.M., de MutsertK.Using sea-ice to calibrate a dynamic trophic model for the Western Antarctic Peninsula. . 2019;14(4):e0214814. doi: 10.1371/journal.pone.021481430939156KleinES, WattersGM. What’s the catch? Profiling the benefits and costs associated with marine protected areas and displaced fishing in the Scotia Sea. . 2020;15(8): e0237425. doi: 10.1371/journal.pone.023742532785268WattersGM, HillSL, HinkeJT, MatthewsJ, ReidK. Decision-making for ecosystem-based management: evaluating options for a krill fishery with an ecosystem dynamics model. . 2013;23(4):710–25. doi: 10.1890/12-1371.123865224HillSL, PhillipsT, AtkinsonA. Potential climate change effects on the habitat of antarctic krill in the weddell quadrant of the southern ocean. . 2013;8(8):e72246. doi: 10.1371/journal.pone.007224623991072KleinES, HillSL, HinkeJT, PhillipsT, WattersGM. Impacts of rising sea temperature on krill increase risks for predators in the Scotia Sea. . 2018;13(1):e0191011. doi: 10.1371/journal.pone.019101129385153HewittRP, WattersG, TrathanPN, CroxallJP, GoebelM, RammD, et al. Options for allocating the precautionary catch limit of krill among small-scale management units in the Scotia Sea. . 2004;11:81–97.PlagányiEE, PuntAE, HillaryR, MorelloEB, ThebaudO, HuttonT, et al. Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity. . 2014;15(1):1–22.HillSL, MatthewsJ. The sensitivity of multiple output statistics to input parameters in a krill-predator-fishery ecosystem dynamics model. . 2013;20: 97–118.Klein ES, Hinke JT, Watters GM. KPFM2: Krill-Predator-Fishery Model; 2019 [cited 29 Nov 2019]. Model code and input files [Internet]. Available from: https://github.com/EmilyKlein/KPFM2.CCAMLR, Conservation Measure 51–01. Precautionary catch limitations on Euphausia superba in Statistical Subareas 48.1, 48.2, 48.3 and 48.4, (2010) Available at: https://www.ccamlr.org/en/measure-51-01-2010.Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Statistical Bulletin. 2018. Report No.: 29. Available from https://www.ccamlr.org/en/meetings/26.Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, R.K. Pachauri and L.A. Meyer (eds). IPCC, Geneva, Switzerland, 151 pp.Watters G, Hinke JT, Reid K, Hill S. KPFM2, Be careful what you ask for–you just might get it. 2006. Report No.: WG-EMM-06/22.Watters G, Hinke JT, Hill S. Developing four plausible parameterisations of FOOSA (a so-called reference set of parameterisations) by conditioning the model on a calendar of events that describes changes in the abundances of krill and their predators in the Scotia Sea. 2008. Report No.: WG-EMM-08/13.HillSL, TrathanPN, AgnewDJ. The risk to fishery performance associated with spatially resolved management of Antarctic krill (Euphausia superba) harvesting. . 2009;66(10):2148–54.Scientific Committee for the Conservation of Antarctic Marine Living Resources (SC-CAMLR). Report of the Thirty-Sixth Meeting of the Commission. Report, 2017 No.: CCAMLR-XXXVI. Available from https://www.ccamlr.org/en/meetings/27.LynchHJ, LaRueMA. First global census of the Adelie Penguin. . 2014;131(4):457–66.Watters G, Hinke JT, Hill S. A risk assessment to advise on strategies for subdividing a precautionary catch limit among small-scale management units during Stage 1 of the staged development of the krill fishery in Subareas 48.1, 48.2, and 48.3. 2008. Report No.: WG-EMM-08/13.Delegations of Argentina and Chile. Domain 1 Marine Protected Area Preliminar Proposal PART A-1: Priority Areas for Conservation. 2017. Report No.: SC-CAMLR-XXVI/17.Delegations of Argentina and Chile. Domain 1 Marine Protected Area Preliminar Proposal PART A-2: MPA Model. 2017. Report No.: SC-CAMLR-XXVI/18.Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Report of the Thirty-Sixth Meeting of the Commission. 2017. Report No.: CCAMLR-XXXVI. Available from https://www.ccamlr.org/en/meetings/26.Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) Conservation Measure 91–03. Protection of the South Orkney Islands southern shelf. 2009. Available at: https://www.ccamlr.org/en/measure-91-03-2009.TrathanPN, ForcadaJ, MurphyEJ. Environmental forcing and Southern Ocean marine predator populations: effects of climate change and variability. . 2007;362(1488):2351–65.ForcadaJ, TrathanPN. Penguin responses to climate change in the Southern Ocean. . 2009;15(7):1618–30.CiminoMA, LynchHJ, SabaVS, OliverMJ. Projected asymmetric response of Adélie penguins to Antarctic climate change. . 2016;6:28785. doi: 10.1038/srep2878527352849HinkeJT, TrivelpieceSG, TrivelpieceWZVariable vital rates and the risk of population declines in Adélie penguins from the Antarctic Peninsula region. . 2017:8, e01666. 01610.01002/ecs01662.01666.HilbornR, StokesK, MaguireJ-J, SmithT, BotsfordLW, MangelM, et al. When can marine reserves improve fisheries management? . 2004;47(3):197–205.CharlesA, WilsonL. Human dimensions of Marine Protected Areas. . 2009;66(1):6–15.AbbottJK, HayneAC. What are we protecting? Fisher behavior and the unintended consequences of spatial closures as a fishery management tool. . 2012;22(3):762–77. doi: 10.1890/11-1319.122645809DinmoreTA, DupliseaDE, RackhamBD, MaxwellDL, JenningsS. Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities. . 2003;60(2):371–80.SumailaUR, GuénetteS, AlderJ, ChuenpagdeeR. Addressing ecosystem effects of fishing using marine protected areas. . 2000;57(3):752–60.PaulyD, ChristensenV, GuénetteS, PitcherTJ, SumailaUR, WaltersCJ, et al. Towards sustainability in world fisheries. . 2002;418:689. doi: 10.1038/nature0101712167876GellFR, RobertsCM. Benefits beyond boundaries: the fishery effects of marine reserves. .18(9):448–55.HalpernBS, WarnerRR. Marine reserves have rapid and lasting effects. . 2002;5(3):361–6.HalpernBS. The impact of marine reserves: Do reserves work and does reserve size matter? . 2003;13(1):S117–S37.LesterSE, HalpernBS, Grorud-ColvertK, LubchencoJ, RuttenbergBI, GainesSD, et al. Biological effects within no-take marine reserves, a global synthesis. . 2009;384:33–46.RobertsCM, BohnsackJA, GellF, HawkinsJP, GoodridgeR. Effects of marine reserves on adjacent fisheries. . 2001;294(5548):1920–3. doi: 10.1126/science.294.5548.192011729316GainesSD, WhiteC, CarrMH, PalumbiSR. Designing marine reserve networks for both conservation and fisheries management. . 2010;107(43):18286–93. doi: 10.1073/pnas.090647310720200311Harrison HugoB, Williamson DavidH, Evans RichardD, Almany GlennR, Thorrold SimonR, Russ GarryR, et al. Larval export from marine reserves and the recruitment benefit for fish and fisheries. . 2012;22(11):1023–8. doi: 10.1016/j.cub.2012.04.00822633811SylvesterZT, BrooksCM. Protecting Antarctica through co-production of actionable science: Lessons from the CCAMLR marine protected area process. . 2019;103720.BanNC, CinnerJE, AdamsVM, MillsM, AlmanyGR, BanSS, et al. Recasting shortfalls of marine protected areas as opportunities through adaptive management. . 2012;22(2):262–71.Delegations of Argentina and Chile. Proposal on a conservation measure establishing a marine protected area in the Domain 1 (Western Antarctic Peninsula and South Scotia Arc). 2018. Report No.: CCAMLR-XXXVII/31.CavanaghRD, HillSL, KnowlandCA, GrantSM. Stakeholder perspectives on ecosystem-based management of the Antarctic krill fishery. . 2016;68:205–11.10.1371/journal.pone.0231954.r001Decision Letter 0Ropert-CoudertYanAcademic Editor2020Yan Ropert-CoudertThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version0
12 Sep 2019
PONE-D-19-22435
Comparing feedback and spatial approaches to advance ecosystem-based fisheries management in a changing Antarctic
PLOS ONE
Dear Dr Klein,
Thank you for submitting your manuscript to PLOS ONE. I have now received reports from two referees who found your work interesting and worthy of publication. They both noted a number of issues that need to be addressed before such a stage can be reached. Referee 2 especially noted the need for more details in the Methods and the construction of your model, especially regarding how krill predator dynamics are modelled (how do you deal with the different taxonomic groups of predators). Therefore, I selected the Major Revision decision and invite you to submit a revised version of the manuscript that addresses all the points raised during the review process. Please note that comments by reviewer 2 are included in the pdf file enclosed to this message.
We would appreciate receiving your revised manuscript by Oct 27 2019 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.
To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols
Please include the following items when submitting your revised manuscript:
A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.
Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.
We look forward to receiving your revised manuscript.
Kind regards,
Yan Ropert-Coudert, PhD
Academic Editor
PLOS ONE
Journal Requirements:
1. When submitting your revision, we need you to address these additional requirements.
Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at
http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf
2. Thank you for stating in your Funding Statement:
[EK was supported by funding from the Pew Charitable Trusts, contract ID #31740. This funder had no role in study design, data collection and analysis, or preparation of the manuscript. Publication under peer review was a requirement of this funding source, but the funder did not take part in deciding where this manuscript would be submitted or any part of the submission process.].
* Please provide an amended statement that declares *all* the funding or sources of support (whether external or internal to your organization) received during this study, as detailed online in our guide for authors at http://journals.plos.org/plosone/s/submit-now. Please also include the statement “There was no additional external funding received for this study.” in your updated Funding Statement.
* Please include your amended Funding Statement within your cover letter. We will change the online submission form on your behalf.
3. We note that Figures [1, 3, 4, 5 and S2, S3, and S4] in your submission contain map/satellite images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.
We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:
You may seek permission from the original copyright holder of Figures [1, 3, 4, 5 and S2, S3, and S4] to publish the content specifically under the CC BY 4.0 license.
We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:
“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”
Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.
In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”
If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.
The following resources for replacing copyrighted map figures may be helpful:
USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/
The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/
Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html
NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/
Landsat: http://landsat.visibleearth.nasa.gov/
USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#
1. Is the manuscript technically sound, and do the data support the conclusions?
The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.
Reviewer #1: Yes
Reviewer #2: Partly
**********
2. Has the statistical analysis been performed appropriately and rigorously?
Reviewer #1: N/A
Reviewer #2: No
**********
3. Have the authors made all data underlying the findings in their manuscript fully available?
The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.
Reviewer #1: No
Reviewer #2: Yes
**********
4. Is the manuscript presented in an intelligible fashion and written in standard English?
PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.
Reviewer #1: Yes
Reviewer #2: Yes
**********
5. Review Comments to the Author
Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)
Reviewer #1: GENERAL COMMENTS
Before reading the whole article, I kind of felt after reading the abstract that the FBM strategies (FBM-Krill) provide greater benefits to predators than the MPA strategy (lines 44 – 48), yet after reading the article I know that’s not the case (this should be made clearer). In fact, the results indicate that MPAs seem to be a better strategy for both predators (except seals) and fisheries – they maintain (slightly increase) catch and increase predator abundance. Of course, it’s not as black and white as this (especially when considering increased fishing in areas of low krill density), but the tone of the article (abstract/results/discuss) comes across as slightly ‘pro-FBM’ and slightly downplaying MPAs, and the authors should consider whether the article needs a slight reframing to be more balanced.
This is particularly important where the overall benefits to penguin abundance under MPAs are the only substantial benefits from any of the three strategies to either biodiversity or fisheries and it should be stated more clearly. Particularly when you determine that total catch was only maintained by the FBM strategies and only slightly increased with MPAs – which starts to raise the question about whether fisheries and fisheries managers would really be interested in applying such strategies if there are also no obvious substantial outcomes for predators (such as the two FBM strats)? It costs time and resources to apply a strategy, so the outcomes/benefits need to be tangible and substantial for industry or CCAMLR to take note (thus it is important to highlight the benefits provided under any strategy).
What kind of seals are you talking about – are they ice-dependant or ice independent? And are you including all 4 Antarctic penguin species?
It would probably be helpful having a flow chart detailing the important steps/variables in the methods (e.g. number of simulations, main indicators, names of the scenarios, timeframe, what the output variable being measured is etc). This would help the reader follow the process.
You didn’t undertake any statistical analysis to determine how much each of the strategies differed from the baseline (No FBM/No MPA), and instead use terms like ‘minimal increases’ etc, which is fine – though it would be useful to provide a scale for the reader to determine what a substantial impact on predator abundance/ fisheries catch is. For example, Fig 2 shows a ~0.35 increase in penguin abundance under the MPA scenario (for 2100) – is this equivalent to a 35% increase in abundance? Which certainly appears to be a substantial increase. If a 35% increase is the wrong interpretation, the scale needs to be clarified as this is how it will be interpreted. You could also consider undertaking statistical analyses to compare the strategies to the baselines (ANOVAs would also allow you to compare between scenarios).
Data availability – it might be helpful for managers and conservationists to have access to the spatial data layers of the SSMU outcomes so that they can utilise them in spatial planning or future studies
SPECIFIC COMMENTS
ABSTRACT
Line 46 – make it ‘outcomes of our second FBM option’ to make it clearer that you are referring to the FBM option rather than the second overall options that you mention in line 40. Maybe reword the line 40 one as ‘strategies’ to keep it consistent with the rest of the article?
INTRODUCTION
Lines 61-65 – Some of these statements could use references
68 – might be useful to give an example of observed climate impacts in the Southern Ocean rather than just referring to refs
95-96 – its not really the decision makers that are robust/adapt to the change, it’s the target/ecosystem that needs to be robust or adapt
97-99 – needs referencing, and would be better split into two sentences.
101-102 – where are the FBM specific objectives/priorities stated by CCAMLR?
104 – what important krill fishing area?
109-112 – beginning with ‘these models’ – split this sentence into 2 – 3 sentences to make it easier to read
117 – should specify that krill density and penguin abundance are the indicators.
118 – how can you project a new MPA based on previously shown ecological benefits (the ecological benefits of a proposed MPA haven’t been established yet)? This needs more explaining/ simplifying to make it less confusing
123 – this is a very broad statement?
Fig1. There is no context to this figure in the intro, it should probably be first referenced in the methods? Subaresas of what?
METHODS
135-138 – what does Fig. 1 have to do with this/ how does it support this sentence?
141 – might be worth providing some additional info on the delay-difference equations
155 – this is where you should introduce Fig 1. Otherwise it seems out of context, have no idea what subareas area etc
156-157 – management mechanism? Or ‘management relevant scale that actions can be implemented at’
157 – what do you mean by the entire model arena? (average of all models?) All 1001 trials? (which I only know about from later in the methods – needs to be explained at first mention)
160 – on what time scale? Plausible future for when?
174 – Can you provide further justification for only using RCP8.5 (I’m not disagreeing that you shouldn’t, but further justification should be provided)
185-187 – what link does estimating the breeding abundance of penguins using remote sensing have to do with developing a FBM? This needs to be made clearer to better justify the claim that both strategies have interest and support from various stakeholders.
188 – what are some of the alternative indicators/ possible approaches?
248 – what recent fishing patterns and from where?
RESULTS
278 – better worded as ‘Relative abundances of krill predators were sensitive to…’?
281 – so actively managing fisheries based on penguin abundance leads to a decline in penguins and seals (therefore managers do worse than if they did nothing) – is that realistic? Coming back to this after reading the discussion section about FBM-Pengs – you explain why the model might have produced the results it did, though do you think the model behaviour is realistic?
Fig 2. When you say ‘All results are referenced to the No FBM or No MPA scenarios.’ do you mean via the dotted line? (if so that should be specified)
Fig 3. Might be useful to have the timeframes labelled on the figure itself. For the broad overall trends presented in Fig 2 – do these only include the average of the SSMUs, or did you model the predator abundance across the subareas too? (can you specify)
Fig 5. Can you include the MPA’s in the figure – hard to compare results against it when it is not labelled/depicted. Also given that figures should stand alone without needing the text, you might need the full text in each figure legend (rather than referring to Fig 3) – but this probably depends on the journals standards.
Fig 6. Dashed line is ‘No FBM’? – you should add that info in (same comment as for Fig 2.)
DISCUSSION
375 – 381 – This is better suited to the introduction. The discussion should be giving the stated insights.
383 – simplify. Suggest something like ‘Decision makers must consider trade-offs in determining the best way to manage the krill industry’
390 – not sure this is an appropriate use of the word offset, at least in a conservation sense (is the decreased penguin abundance really adequately offset by a lower probability of fishing in areas of low krill density?)
393 – But only a single seal population declined (based on figure 5 and 6)?
391 & 394 – when you are talking about areas of low krill density, are you referring to the ‘threshold violation’ results from Fig 2? This should be made clearer in both the results and the discussion, else it appears that there are no results depicting low krill density and it is hard for the reader to find support for this statement (and either way there are no spatial results indicating where fishing occurred in low krill density areas?)
397 – 399 – round-about way of writing the sentence, please simplify
401 – 403 – Suggesting that managing Krill via FBM as a conservation strategy for predators is somewhat misleading – there were only extremely minor benefit to penguins using FBM-Krill and declines in seals (Figure 2) and there was no benefit to the fishery (Fig 2 & your statement in #299). Yet it would cost money to apply FBM and update it each time, and this is resources that could go to managing predator populations in a more effective way?
405 – What trade-offs? Please specify.
409 – 411 – Good, this is an important finding
440 – 445 – Good, this is a good conclusion
449 – potentially reword? ‘It is widely believed that routine application …’
458 – people? Fisheries is a better term
489 – Words like panacea and pathological can be simplified
516 – The sentence beginning with ‘In fact’ doesn’t make sense (before CCAMLR?)
520 – 522 – Sentence doesn’t make sense – something about the structure/position of the commas.
523 – instead of ‘steps’, something like ‘advancement toward’
REFERENCES
34/35 – you generally can’t base statements on things that haven’t been published or are not in press yet (see lines 118/119 – I went to look at the reference because I wanted to know more)? Are these in press? (what does forthcoming mean?)
Reviewer #2: It is difficult to fully evaluate this manuscript as essential methodological detail is missing. The authors cite earlier papers in lieu of providing these details but I don't think this appropriate for basic information that is essential to understanding what the authors have done and, hence, for evaluating the results and conclusions. This is why I have answered "Partly" and "No" to questions 1 and 2.
**********
6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.
If you choose “no”, your identity will remain anonymous but your review may still be made public.
Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.
Reviewer #1: No
Reviewer #2: No
[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]
While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.
Submitted filename: pone-D-19-22435.pdf
10.1371/journal.pone.0231954.r002Author response to Decision Letter 0Submission Version1
30 Nov 2019
We thank the editor and the two reviewers for their comments on our initial submission of this manuscript. Overall, the comments have provided insightful and valuable feedback that has improved the manuscript, and we hope we have addressed their concerns. We have edited the manuscript to address concerns, and, as the comments and our specific responses are lengthy, have included these as a separate file in the revision.
10.1371/journal.pone.0231954.r003Decision Letter 1Ropert-CoudertYanAcademic Editor2020Yan Ropert-CoudertThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version1
7 Jan 2020
PONE-D-19-22435R1
Comparing feedback and spatial approaches to advance ecosystem-based fisheries management in a changing Antarctic
PLOS ONE
Dear Dr Klein,
Thank you for submitting your revised manuscript to PLOS ONE. After consulting with one of the previous referees I would like to invite you to revise once more your paper so as to further improved your manuscript and get it ready for publication. I have selected the Minor Revision decision even though it may be slightly time consuming (hopefully not much) for you to address the final comments. I concur with the referee that it is necessary to give a bit of context to the Code that you've published on GitHub and that additional details on the Methodology are therefore needed to ensure that readers could use your approach without having to search other publications to do so. I would thus ask you to follow the recommendation of the referee to provide a bit of context in a Supplementary material so as not to make the reading too cumbersome. I understand that this entails extra work but, besides improving the chances of your paper to be used and cited, this would also allow for your manuscript to meet with the third Plos One criterion "Experiments, statistics, and other analyses are performed to a high technical standard and are described in sufficient detail."
We would appreciate receiving your revised manuscript by Feb 21 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.
To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols
Please include the following items when submitting your revised manuscript:
A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.
Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.
We look forward to receiving your revised manuscript.
Kind regards,
Yan Ropert-Coudert, PhD
Academic Editor
PLOS ONE
[Note: HTML markup is below. Please do not edit.]
Reviewers' comments:
Reviewer's Responses to Questions
Comments to the Author
1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.
Reviewer #2: (No Response)
**********
2. Is the manuscript technically sound, and do the data support the conclusions?
The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.
Reviewer #2: Yes
**********
3. Has the statistical analysis been performed appropriately and rigorously?
Reviewer #2: Yes
**********
4. Have the authors made all data underlying the findings in their manuscript fully available?
The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.
Reviewer #2: Yes
**********
5. Is the manuscript presented in an intelligible fashion and written in standard English?
PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.
Reviewer #2: Yes
**********
6. Review Comments to the Author
Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)
Reviewer #2: I appreciate the Authors' detailed response to my review and generally agree with their fundamental points regarding: 1) the length of additional context required to fully describe their previously published model; and 2) the citation of previous studies to support deriving studies/publications. Regarding point 2, I would add that any published study should be sufficiently self-contained that readers (and reviewers) can understand its methodology, at least conceptually, and therefore be able to judge the value of the results and conclusions drawn. I found some methodological context lacking in the original manuscript, and, as I stated in my original review, did not have access at that time to the preceding publications in order to better understand the model structure. In situations where essential aspects of a submitted paper's methodology are published elsewhere, Authors should provide the additional publication(s) as supporting documents to aid review and/or aggregate and condense those details in Supplementary Information. Both of these practices are commonplace and a courtesy to reviewers and interested readers by not placing upon them the burden of searching for the information across (potentially) multiple sources. Providing such detail as Supporting Information also enhances a study's reproducibility, which is also a fundamental element of publication (see my comment on line 168, below). Citation of the preceding studies, as the Authors stated, serves to assure readers that the foundational work has been vetted by peer review.
I do realise this approach places an extra burden on getting work published, at times I have grudgingly laboured under this burden myself, but it generally enhances readership and citation of numerically intensive studies.
Upon reading both the Author's Response to my original review and their revised manuscript, I am satisfied that most of the issues I originally raised have been addressed. Figure 2 nicely provides more context on the overall approach, and the discussion of model assumptions is a welcome addition.
A fairly minor point: given the two FBM strategies considered rely on updating catch limits based on changes in either krill density or penguin abundance, I still find it confusing that the Authors state that they "...focus on penguins and seals to simplify results..." (lines 152-153). I understand that the ecosystem model includes all the major krill predator groups but I can find no prior rationale why seals need be considered in these results when the FBM strategies are based on either krill or penguin indicators. Presumably, the predicted relative abundance of seals under the differing strategies provides a comparison to penguins, thereby giving a sense of how each strategy may benefit (or not) the broader ecosystem. An extra sentence in the last paragraph of the Introduction is probably all that is needed to clarify.
Specific Comments:
Figure 1 - it would be helpful to label the SSMU's 1-15, even if a smaller font is required. Doing so would then give spatial context to Table 1, which could be pointed out in the caption to Table 1.
Line 163 - have these data been published? if so, would be useful to cite.
Line 168 - I am pleased to see the code is now provided in a GitHub repository with a detailed description and analysis guide for readers interested in reproducing or extending the Authors' work. The Authors probably did not intend this but it's useful to point out that while such code repositories are enormously helpful for reproducibility, they are not a replacement for a sufficient explanation of a study's methodology.
**********
7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.
If you choose “no”, your identity will remain anonymous but your review may still be made public.
Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.
Reviewer #2: No
[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]
While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.
10.1371/journal.pone.0231954.r004Author response to Decision Letter 1Submission Version2
3 Apr 2020
This text has also been included as a separate document in the re-submission packet.
Response to reviewer comments
We thank the editor and the reviewer for their continued efforts with our work, and we hope we have addressed the final needs on this paper. We appreciate their time and support.
Reviewer #1:
Comment: I appreciate the Authors' detailed response to my review and generally agree with their fundamental points regarding: 1) the length of additional context required to fully describe their previously published model; and 2) the citation of previous studies to support deriving studies/publications. Regarding point 2, I would add that any published study should be sufficiently self-contained that readers (and reviewers) can understand its methodology, at least conceptually, and therefore be able to judge the value of the results and conclusions drawn. I found some methodological context lacking in the original manuscript, and, as I stated in my original review, did not have access at that time to the preceding publications in order to better understand the model structure. In situations where essential aspects of a submitted paper's methodology are published elsewhere, Authors should provide the additional publication(s) as supporting documents to aid review and/or aggregate and condense those details in Supplementary Information. Both of these practices are commonplace and a courtesy to reviewers and interested readers by not placing upon them the burden of searching for the information across (potentially) multiple sources. Providing such detail as Supporting Information also enhances a study's reproducibility, which is also a fundamental element of publication (see my comment on line 168, below). Citation of the preceding studies, as the Authors stated, serves to assure readers that the foundational work has been vetted by peer review.
I do realise this approach places an extra burden on getting work published, at times I have grudgingly laboured under this burden myself, but it generally enhances readership and citation of numerically intensive studies.
Upon reading both the Author's Response to my original review and their revised manuscript, I am satisfied that most of the issues
I originally raised have been addressed. Figure 2 nicely provides more context on the overall approach, and the discussion of model assumptions is a welcome addition.
Response: We again thank this reviewer for their time and for continuing to engage with us to improve this manuscript. We also agree with their points here, and the importance of manuscripts standing alone – the discussion here illuminates, at least to us, a challenge in publication that should be more deeply considered, and we thank this Reviewer for urging us to do so. We have added additional text to the manuscript and Supporting Information, as well as two tables and READ ME files to the SI. We are hopeful this will work; if additional details are necessary, we ask for more specific instruction on what would be useful. We sincerely wish to adequately address these concerns but it remains somewhat unclear to us what is needed as well as also possible without recreating the previously published text of Watters et al. (2013).
Comment: A fairly minor point: given the two FBM strategies considered rely on updating catch limits based on changes in either krill density or penguin abundance, I still find it confusing that the Authors state that they "...focus on penguins and seals to simplify results..." (lines 152-153). I understand that the ecosystem model includes all the major krill predator groups but I can find no prior rationale why seals need be considered in these results when the FBM strategies are based on either krill or penguin indicators. Presumably, the predicted relative abundance of seals under the differing strategies provides a comparison to penguins, thereby giving a sense of how each strategy may benefit (or not) the broader ecosystem. An extra sentence in the last paragraph of the Introduction is probably all that is needed to clarify.
Response: We find this comment quite important, and critical that we clarify. First, the Reviewer is correct in their assessment: indicators are useful as they can simplify management objectives and approach, but there will still be larger, ecosystem outcomes of changes in management beyond the indicator species (of course). Here, outcomes for seals allow us some limited insight into how using one indicator species may have implications for others – i.e. comparing consequences for seals to those for penguins, as the Reviewer surmises. Second, our reasoning for seals and penguins in the main text and having other krill predators in Supporting is not to say these other species are less important or are not also a window into impacts on additional outcomes, but to maintain a manageable set of results in the text, given our figures are already extensive with two species – i.e. that is a logistical consideration only.
It is important these points are clear for readers, and we have addressed the text to hopefully make them so (starting line 142).
Comment: Figure 1 - it would be helpful to label the SSMU's 1-15, even if a smaller font is required. Doing so would then give spatial context to Table 1, which could be pointed out in the caption to Table 1.
Response: We have altered Figure 1, increasing the map size and using red to help the SSMU numbers stand out. We have also noted it in the caption for Table 1 as helpfully suggested.
Comment: Line 163 - have these data been published? if so, would be useful to cite.
Response: We believe this comment refers to the reference set of four parameterization, which is indeed published and the citation is referenced (Watters et al., reference 39 in the main text).
Comment: Line 168 - I am pleased to see the code is now provided in a GitHub repository with a detailed description and analysis guide for readers interested in reproducing or extending the Authors' work. The Authors probably did not intend this but it's useful to point out that while such code repositories are enormously helpful for reproducibility, they are not a replacement for a sufficient explanation of a study's methodology.
Response: We agree, and have added additional text in the main manuscript and Supporting Information to this effect.
10.1371/journal.pone.0231954.r005Decision Letter 2Ropert-CoudertYanAcademic Editor2020Yan Ropert-CoudertThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version2
6 Apr 2020
Comparing feedback and spatial approaches to advance ecosystem-based fisheries management in a changing Antarctic
PONE-D-19-22435R2
Dear Dr. Klein,
We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.
Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.
Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.
If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.
With kind regards,
Yan Ropert-Coudert, PhD
Academic Editor
PLOS ONE
Additional Editor Comments (optional):
Reviewers' comments:
10.1371/journal.pone.0231954.r006Acceptance letterRopert-CoudertYanAcademic Editor2020Yan Ropert-CoudertThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
26 Aug 2020
PONE-D-19-22435R2
Comparing feedback and spatial approaches to advance ecosystem-based fisheries management in a changing Antarctic
Dear Dr. Klein:
I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.
If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.
If we can help with anything else, please email us at plosone@plos.org.
Thank you for submitting your work to PLOS ONE and supporting open access.