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Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models

Diversity and Distributions

By:
, , and
DOI: 10.1111/ddi.12059

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Abstract

Aim: Rare aquatic species are a substantial component of biodiversity, and their conservation is a major objective of many management plans. However, they are difficult to assess, and their optimal habitats are often poorly known. Methods to effectively predict the likely locations of suitable rare aquatic species habitats are needed. We combine two modelling approaches to predict occurrence and general abundance of several rare fish species. Location: Allegheny watershed of western New York State (USA) Methods: Our method used two empirical neural network modelling approaches (species specific and assemblage based) to predict stream-by-stream occurrence and general abundance of rare darters, based on broad-scale habitat conditions. Species-specific models were developed for longhead darter (Percina macrocephala), spotted darter (Etheostoma maculatum) and variegate darter (Etheostoma variatum) in the Allegheny drainage. An additional model predicted the type of rare darter-containing assemblage expected in each stream reach. Predictions from both models were then combined inclusively and exclusively and compared with additional independent data. Results Example rare darter predictions demonstrate the method's effectiveness. Models performed well (R2 ≥ 0.79), identified where suitable darter habitat was most likely to occur, and predictions matched well to those of collection sites. Additional independent data showed that the most conservative (exclusive) model slightly underestimated the distributions of these rare darters or predictions were displaced by one stream reach, suggesting that new darter habitat types were detected in the later collections. Main conclusions Broad-scale habitat variables can be used to effectively identify rare species' habitats. Combining species-specific and assemblage-based models enhances our ability to make use of the sparse data on rare species and to identify habitat units most likely and least likely to support those species. This hybrid approach may assist managers with the prioritization of habitats to be examined or conserved for rare species.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models
Series title:
Diversity and Distributions
DOI:
10.1111/ddi.12059
Volume
19
Issue:
5-6
Year Published:
2013
Language:
English
Publisher:
Wiley
Contributing office(s):
Great Lakes Science Center
Description:
15 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Diversity and Distributions
First page:
503
Last page:
517
Country:
United States
State:
New York