Dataset Information

Species distribution models of European Turtle Doves in Germany are more reliable with presence only rather than presence absence data.

ABSTRACT: Species distribution models (SDMs) can help to describe potential occurrence areas and habitat requirements of a species. These data represent key information in ecology and conservation, particularly for rare or endangered species. Presence absence (PA) and presence only (PO) records of European Turtle Doves Streptopelia turtur in Germany were used to run SDMs, whilst climate and land coverage variables provided environmental information. GLM (Generalised Linear model), GBM (Generalised Boosted model), CTA (Classification Tree analysis), SRE (Surface Range Envelope) and RF (Random Forests) algorithms were run with both datasets. Best model quality was obtained with PO in the RF algorithm (AUC 0.83). PA and PO probability maps differed substantially, but both excluded mountainous regions as potential occurrence areas. However, PO probability maps were more discriminatory and highlighted a possible distribution of Turtle Doves near Saarbrucken, west of Dusseldorf, in the Black Forest lowlands and Lusatia. Mainly, the climate variables 'minimum temperature in January' and 'precipitation of the warmest quarter' shaped these results, but variables like soil type or agricultural management strategy could improve future SDMs to specify local habitat requirements and develop habitat management strategies. Eventually, the study demonstrated the utility of PO data in SDMs, particularly for scarce species.

SUBMITTER: Marx M

PROVIDER: S-EPMC6237818 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Species distribution models of European Turtle Doves in Germany are more reliable with presence only rather than presence absence data.

Marx Melanie M Quillfeldt Petra P

Scientific reports 20181115 1

Species distribution models (SDMs) can help to describe potential occurrence areas and habitat requirements of a species. These data represent key information in ecology and conservation, particularly for rare or endangered species. Presence absence (PA) and presence only (PO) records of European Turtle Doves Streptopelia turtur in Germany were used to run SDMs, whilst climate and land coverage variables provided environmental information. GLM (Generalised Linear model), GBM (Generalised Boosted ...[more]

PMID: 30442997

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Project description:BackgroundEcological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs.MethodsWe simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure.ResultsOverall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA's error rates under almost all scenarios.ConclusionWe encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.

Project description:The availability of spatially referenced environmental data and species occurrence records in online databases enable practitioners to easily generate species distribution models (SDMs) for a broad array of taxa. Such databases often include occurrence records of unknown reliability, yet little information is available on the influence of data quality on SDMs generated for rare, elusive, and cryptic species that are prone to misidentification in the field. We investigated this question for the fisher (Pekania pennanti), a forest carnivore of conservation concern in the Pacific States that is often confused with the more common Pacific marten (Martes caurina). Fisher occurrence records supported by physical evidence (verifiable records) were available from a limited area, whereas occurrence records of unknown quality (unscreened records) were available from throughout the fisher's historical range. We reserved 20% of the verifiable records to use as a test sample for both models and generated SDMs with each dataset using Maxent. The verifiable model performed substantially better than the unscreened model based on multiple metrics including AUCtest values (0.78 and 0.62, respectively), evaluation of training and test gains, and statistical tests of how well each model predicted test localities. In addition, the verifiable model was consistent with our knowledge of the fisher's habitat relations and potential distribution, whereas the unscreened model indicated a much broader area of high-quality habitat (indices > 0.5) that included large expanses of high-elevation habitat that fishers do not occupy. Because Pacific martens remain relatively common in upper elevation habitats in the Cascade Range and Sierra Nevada, the SDM based on unscreened records likely reflects primarily a conflation of marten and fisher habitat. Consequently, accurate identifications are far more important than the spatial extent of occurrence records for generating reliable SDMs for the fisher in this region. We strongly recommend that practitioners avoid using anecdotal occurrence records to build SDMs but, if such data are used, the validity of resulting models should be tested with verifiable occurrence records.

Dataset Information

Species distribution models of European Turtle Doves in Germany are more reliable with presence only rather than presence absence data.

Publications

Species distribution models of European Turtle Doves in Germany are more reliable with presence only rather than presence absence data.

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