Open AccessUsing Gradient Forests to summarize patterns in species turnover across large spatial scales and inform conservation planning
Author(s) -
Stephenson Fabrice,
Leathwick John R.,
Geange Shane W.,
Bulmer Richard H.,
Hewitt Judi E.,
Anderson Owen F.,
Rowden Ashley A.,
Lundquist Carolyn J.
Publication year - 2018
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.12787
Subject(s) - demersal fish , beta diversity , demersal zone , biodiversity , spatial ecology , sampling (signal processing) , scale (ratio) , environmental science , sample (material) , marine spatial planning , marine protected area , spatial analysis , spatial variability , pelagic zone , ecology , fish <actinopterygii> , geography , computer science , statistics , environmental resource management , cartography , fishery , mathematics , habitat , biology , remote sensing , filter (signal processing) , chemistry , chromatography , computer vision
Aim Producing quantitative descriptions of large‐scale biodiversity patterns is challenging, particularly where biological sampling is sparse or inadequate. This issue is particularly problematic in marine environments, where sampling is both difficult and expensive, often resulting in patchy and/or uneven coverage. Here, we evaluate the ability of Gradient Forest ( GF ) modelling to describe broad‐scale marine biodiversity patterns, using a large dataset that also provided opportunity to investigate the effects of sample size on model stability. Location New Zealand's Extended Continental Shelf to depths of 2,000 m. Methods GF models were used to analyse and predict spatial patterns of demersal fish species turnover (beta diversity) using an extensive demersal fish dataset (>27,000 research trawls) and high‐resolution environmental data layers (1 km 2 grid resolution). GF models were fitted using various sized, mutually exclusive subsets of the demersal fish data to explore the effect of variation in numbers of training observations on model performance and stability. A final GF model using 13,917 samples was used to transform the environmental layers, which were then classified to produce 30 spatial groups; the ability of these groups to identify fish samples with similar composition was evaluated using independent sample data. Results Model fitting using varying sized subsets of the data indicated only minimal changes in model outcomes when using >7,000 observations. A multiscale spatial classification of marine environments created using results from a final GF model fitted using ~14,000 samples was highly effective at summarizing spatial variation in both fish assemblage composition and species turnover. Main conclusions The hierarchical nature of the classification supports its use at varying levels of classification detail, which is advantageous for conservation planning at differing spatial scales. This approach also facilitates the incorporation of information on intergroup similarities into conservation planning, allowing greater protection of distinctive groups likely to support unusual assemblages of species.