ReviewECOLOGY

Standards for distribution models in biodiversity assessments

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Science Advances  16 Jan 2019:
Vol. 5, no. 1, eaat4858
DOI: 10.1126/sciadv.aat4858
  • Fig. 1 Uses of SDMs.

    Classification of published species distribution modeling studies by (A) type of biodiversity assessment accomplished with the trend in the numbers of studies shown over time and (B) purpose of the model (see glossary in text S4). In (A), the trend for translocation is very similar to that of restoration, and hence is hardly visible. The classification is based on a random sample of 400 papers (of 6483 identified articles mentioning statistical models of species distributions); 238 of the randomly selected papers used SDMs and were included in this analysis. Details on the literature search and analyses appear in text S1, figs. S1.1, S1.2, S1.3, and S1.4, and tables S1.1, S1.2, and S1.3.

  • Fig. 2 Steps in biodiversity assessments.

    Assessment process flow as typically implemented by international and national initiatives on biodiversity and/or climate change (e.g., IPBES, IPCC, IUCN, and national governments) and the suggested addition of agreed-upon (and updated) standards to ensure the adequacy of studies feeding into the assessments. Blue arrows and hollow boxes represent the current procedure, and red arrows and green-filled boxes represent the suggested additional steps.

  • Fig. 3 Best-practice standards achieved by 400 species distribution modeling studies (1995–2015).

    The lines show quantiles of scores for each issue across all studies (blue = top 90% of studies; red = top 50%). See tables S2.1, S2.2, S2.3, and S2.4 for definition of standards. The area inside the respective polygon (defined by the blue and red lines) is used as a metric of overall quality of the models. The greater the area inside the polygon, the higher the overall scores for the standards. Details on the selection and scoring of articles are provided in text S3 and table S3.1.

  • Fig. 4 Changes in best-practice standards of species distribution modeling studies over time.

    The diagrams show the results of ordinal regression using “Year” as a continuous variable and the four key aspects of modeling as effects (including an interaction). The analysis was implemented for a sample of 400 modeling studies used for various biodiversity assessments between 1995 and 2015. (A) Values near zero on the x axis represent no change in standards over time, positive values indicate improvement, and bars are 95% credible intervals. (B) Shading represents the 95% credible intervals. Note clear increase in the number of acceptable studies regarding model building as well as lesser increases in the quality of studies with regard to model evaluation and response data. Details on the selection and scoring of articles are provided in text S3 and table S3.1. Figure S1.5 shows the raw scores used.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/1/eaat4858/DC1

    Supplementary Text

    Text S1. Uses of models in biodiversity assessments.

    Text S2. Guidelines for scoring models in biodiversity assessments.

    Text S2.1. Guidelines for the response variable.

    Text S2.1A. Sampling of response variables.

    Text S2.1B. Identification of taxa.

    Text S2.1C. Spatial accuracy of response variable.

    Text S2.1D. Environmental extent across which response variable is sampled.

    Text S2.1E. Geographic extent across which response variable is sampled (includes occurrence data and absence, pseudo-absence, or background data).

    Text S2.2. Guidelines for the predictor variables.

    Text S2.2A. Selection of candidate variables.

    Text S2.2B. Spatial and temporal resolution of predictor variables.

    Text S2.2C. Uncertainty in predictor variables.

    Text S2.3. Guidelines for model building.

    Text S2.3A. Model complexity.

    Text S2.3B. Treatment of bias and noise in response variables.

    Text S2.3C. Treatment of collinearity.

    Text S2.3D. Dealing with modeling and parameter uncertainty.

    Text S2.4. Guidelines for model evaluation.

    Text S2.4A. Evaluation of model assumptions.

    Text S2.4B. Evaluation of model outputs.

    Text S2.4C. Measures of model performance.

    Text S3. Scoring a representative sample of the literature according to the guidelines.

    Text S4. Glossary.

    Fig. S1.1. Classification of 400 randomly sampled papers applying SDMs to biodiversity assessments according to the number and taxonomic group of species modeled.

    Fig. S1.2. Classification of 400 randomly sampled papers applying SDMs to biodiversity assessments according to the continent and ecological realm of focus.

    Fig. S1.3. Accumulated percentage of papers reviewed falling in different classes as the size of the random sample is increased.

    Fig. S1.4. The continents used to classify papers applying SDMs to biodiversity assessments.

    Fig. S1.5. Frequencies of scores of different categories of issues assessed.

    Fig. S1.6. Differences between scores obtained in the first assessment of the studies and the second independent reevaluation by a different assessor.

    Fig. S1.7. Changes in species distribution modeling standards over time (1995–2015).

    Fig. S1.8. Magnitude of standard deviations (0, no error; 1, maximum error) between first and second independent scoring of the studies over annual steps for each aspect and issue judged.

    Table S1.1. Search terms used to select papers for the literature characterization.

    Table S1.2. Classification of the purpose for which SDMs are used.

    Table S1.3. Classification of the conservation applications of SDMs.

    Table S2.1. Guidelines—Response variable.

    Table S2.2. Guidelines—Predictor variables.

    Table S2.3. Guidelines—Model building.

    Table S2.4. Guidelines—Model evaluation.

    Table S3.1. Search terms used to select papers using SDMs for biodiversity assessments, for the purpose of scoring according to the guidelines.

    References (85163)

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Text
    • Text S1. Uses of models in biodiversity assessments.
    • Text S2. Guidelines for scoring models in biodiversity assessments.
    • Text S2.1. Guidelines for the response variable.
    • Text S2.1A. Sampling of response variables.
    • Text S2.1B. Identification of taxa.
    • Text S2.1C. Spatial accuracy of response variable.
    • Text S2.1D. Environmental extent across which response variable is sampled.
    • Text S2.1E. Geographic extent across which response variable is sampled (includes occurrence data and absence, pseudo-absence, or background data).
    • Text S2.2. Guidelines for the predictor variables.
    • Text S2.2A. Selection of candidate variables.
    • Text S2.2B. Spatial and temporal resolution of predictor variables.
    • Text S2.2C. Uncertainty in predictor variables.
    • Text S2.3. Guidelines for model building.
    • Text S2.3A. Model complexity.
    • Text S2.3B. Treatment of bias and noise in response variables.
    • Text S2.3C. Treatment of collinearity.
    • Text S2.3D. Dealing with modeling and parameter uncertainty.
    • Text S2.4. Guidelines for model evaluation.
    • Text S2.4A. Evaluation of model assumptions.
    • Text S2.4B. Evaluation of model outputs.
    • Text S2.4C. Measures of model performance.
    • Text S3. Scoring a representative sample of the literature according to the guidelines.
    • Text S4. Glossary.
    • Fig. S1.1. Classification of 400 randomly sampled papers applying SDMs to biodiversity assessments according to the number and taxonomic group of species modeled.
    • Fig. S1.2. Classification of 400 randomly sampled papers applying SDMs to biodiversity assessments according to the continent and ecological realm of focus.
    • Fig. S1.3. Accumulated percentage of papers reviewed falling in different classes as the size of the random sample is increased.
    • Fig. S1.4. The continents used to classify papers applying SDMs to biodiversity assessments.
    • Fig. S1.5. Frequencies of scores of different categories of issues assessed.
    • Fig. S1.6. Differences between scores obtained in the first assessment of the studies and the second independent reevaluation by a different assessor.
    • Fig. S1.7. Changes in species distribution modeling standards over time (1995–2015).
    • Fig. S1.8. Magnitude of standard deviations (0, no error; 1, maximum error) between first and second independent scoring of the studies over annual steps for each aspect and issue judged.
    • Table S1.1. Search terms used to select papers for the literature characterization.
    • Table S1.2. Classification of the purpose for which SDMs are used.
    • Table S1.3. Classification of the conservation applications of SDMs.
    • Table S2.1. Guidelines—Response variable.
    • Table S2.2. Guidelines—Predictor variables.
    • Table S2.3. Guidelines—Model building.
    • Table S2.4. Guidelines—Model evaluation.
    • Table S3.1. Search terms used to select papers using SDMs for biodiversity assessments, for the purpose of scoring according to the guidelines.

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    Correction (11 April 2019): The sources mentioned in the Supplementary Materials were mistakenly omitted in the original publication. The PDF and HTML versions of the paper were updated to include these sources in the manuscript�s reference list.

    The original version is accessible here.

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