Open AccessMultiple sources of uncertainty affect metrics for ranking conservation risk under climate change
Author(s) -
Wright Amber N.,
Hijmans Robert J.,
Schwartz Mark W.,
Shaffer H. Bradley
Publication year - 2015
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12257
Subject(s) - climate change , ecological niche , environmental niche modelling , niche , ranking (information retrieval) , ecology , habitat , species distribution , range (aeronautics) , environmental science , greenhouse gas , environmental resource management , occupancy , rank (graph theory) , climate model , econometrics , computer science , mathematics , biology , materials science , combinatorics , machine learning , composite material
Abstract Aim Ecological niche modelling is one of the main tools that allows for the incorporation of climate change effects into conservation planning. For example, ecological niche model predictions can be used to rank species by degree of predicted future habitat loss. While many studies have considered how different modelling decisions contribute to uncertainty in niche model outputs, here we evaluate how metrics used to rank species by conservation risk respond to the choice of global climate models, greenhouse gas emission scenarios, suitable versus unsuitable threshold values, and the degree of model complexity. Location California, USA . Methods We built ecological niche models for 153 species of reptiles and amphibians. Reduced complexity models were compared to default complexity models using AIC c to select climate variables and tune Maxent' s built‐in regularization parameter. We predicted the distribution of climatically suitable habitat under future (2041–2060) climate conditions according to 11 global climate models, four representative concentration pathways, and three threshold values. Two metrics to rank species by predicted future loss of climatically suitable habitat were calculated for each set of modelling decisions. To determine the effects of modelling decisions on rankings, we used mixed models. Results Our results indicate that while individual modelling decisions had relatively small effects on species ranks alone, in combination, they lead to very different conservation assessments. Main conclusions We recommend that a wide range of modelling decisions be explored and that variation in ranks across runs be reported as a first step in identifying the uncertainty in rank metrics used for assessing conservation risk under changing, but uncertain climate predictions.