Details:

Journal Title:
Diversity and Distributions
Personal Author:
Johnston, Miriam R. ; Elmore, Andrew J. ; Mokany, Karel ; Lisk, Matthew ; Fitzpatrick, Matthew C.
Johnston, Miriam R. ; Elmore, Andrew J. ; Mokany, Karel ; Lisk, Matthew ; Fitzpatrick, Matthew C. Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research) ; Sea Grant
OAR (Oceanic and Atmospheric Research) ; Sea Grant Less -
Sea Grant Program:
MDU (Maryland Sea Grant)
Description:
Aim

In order to map patterns of biodiversity in support of conservation efforts, statistical models require environmental variables with full coverage across the study area, typically in the form of gridded surfaces derived from GIS, remote sensing or via interpolation. However, derived variables may not be as physiologically relevant or as representative of on-the-ground conditions as field-measured variables. Here, we examine differences in the abilities of derived and field-measured variables to explain and predict biogeographical patterns of freshwater fish and benthic invertebrate communities.

Location

Maryland first- through fourth-order streams, USA.

Methods

We fit generalized dissimilarity models to fish and benthic invertebrate occurrence data (n = 2,165 site-years sampled over 18 years) using one set of field-measured predictors collected concurrent and collocated with faunal sampling, two sets of derived predictors (one representing local and one representing upstream conditions) or a combination. We then compared how well models explained and predicted spatial turnover in taxonomic composition (beta diversity).

Results

For all regions (four physiographic regions and the state as a whole) and for both fish and benthic invertebrates, models fit with field-measured variables were more explanatory and usually more predictive than models fit with derived variables. Within the category of derived predictors, those accounting for upstream conditions were more explanatory and predictive than local-scale versions.

Main conclusions

Although derived variables are most commonly used to describe and map biodiversity, they may be broadly inferior to field-measured variables as predictors in low-order stream biodiversity models. Collection of field-measured data and development of derived data that consider upstream conditions and capture physiologically relevant environmental characteristics are likely to improve our capability to predict and explain spatial patterns of low-order stream biodiversity.


Keywords:
[+]
Biodiversity Biogeography Ecology
Source:
Diversity and Distributions 23(9): 1054-1066
DOI:
https://doi.org/10.1111/ddi.12598
Sea Grant Document Number:
MDU-R-17-006
Document Type:
Journal Article
Funding:
Grant no. NA10OAR4170072
Place as Subject:
Maryland
Rights Information:
Other
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:db943abcf6695837e8feadeee718811f3f95286eec30d2812ca8ade4009c46ec
Download URL:
https://repository.library.noaa.gov/view/noaa/43791/noaa_43791_DS1.pdf
File Type:

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