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From a line in the sand to a landscape of decisions: a hierarchical diversity decision framework for estimating and communicating biodiversity loss along anthropogenic gradients
Author(s) -
Voss Kristofor A.,
King Ryan S.,
Bernhardt Emily S.
Publication year - 2015
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12379
Subject(s) - biodiversity , benchmark (surveying) , streams , environmental science , range (aeronautics) , water quality , taxon , abundance (ecology) , environmental resource management , sensitivity (control systems) , ecology , geography , computer science , biology , cartography , engineering , aerospace engineering , electronic engineering , computer network
Summary When setting water quality criteria, managers must choose thresholds for stressors that are protective of aquatic biodiversity. Setting such thresholds requires making implicit judgments about the degree of biodiversity loss that managers are willing to accept. We present a new modelling approach, the hierarchical diversity decision framework ( H i DD e F ) that explicitly communicates the sensitivity of water quality benchmarks to these implicit judgments. We apply H i DD e F to a data set of stream macroinvertebrate abundances across 218 sites in south‐western W est V irginia, USA , where alkaline mine drainage increases streamwater conductivity and leads to the loss of sensitive taxa throughout regional river networks. By integrating responses of individual taxa within a flexible hierarchical framework, H i DD e F reliably predicts macroinvertebrate assemblages across the full range of conductivities observed in the training data set but requires only a fraction (~10%) of the sites required in previous studies. Hi DD e F results suggest that the current conductivity benchmark (300 μS cm −1 ) for regional streams translates to 50% loss in abundance for at least one‐quarter of regional macroinvertebrate taxa. Hi DD eF produces a ‘decision landscape’ that allows decision‐makers to assess sensitivity of proposed benchmarks to their choice of protective level. Hi DD eF allows users to investigate both individual‐ and community‐level responses to environmental gradients and generates output that includes a comprehensive summary of uncertainty in model parameters.