Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

Franklin, Janet; Wejnert, Katherine E.; Hathaway, Stacie A.; Rochester, Carlton J.; Fisher, Robert N.

doi:10.1111/j.1472-4642.2008.00536.x

Cited by 119 publications

(105 citation statements)

References 67 publications

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“…Any of these factors offered statistically good explanations for their distribution. In contrast, the common and widespread species were found over a range of conditions for all environmental factors we considered, and it was difficult to come up with any explanation that performed as well as the restricted species (see also Franklin et al, 2009). …”

Section: Resultsmentioning

confidence: 91%

An evaluation of environmental factors affecting species distributions

Ashcroft

French

Chisholm

2011

Ecological Modelling

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Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species' distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model. distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an Area Under the Curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected ...

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Section: Resultsmentioning

confidence: 91%

An evaluation of environmental factors affecting species distributions

Ashcroft

French

Chisholm

2011

Ecological Modelling

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“…Nevertheless, SDMs are still imperfect representations of actual species distributions, both because of omitted explanatory variables and because of small or non-systematic spatial samples of species presence and abundance (Dennis and Thomas 2000, Kadmon et al 2004, Barry and Elith 2006, Fourcade et al 2014. The standard measure of SDM accuracy conflates errors of omission and commission; the latter (in which a location believed to be suitable for a species is actually not) is particularly problematic for conservation planning, especially given that they tend to be spatially autocorrelated (e.g., Franklin et al 2009). Quantifications of these errors (Heikkinen et al 2006, Elith andLeathwick 2009) will be needed to make reliable predictions for conservation planning.…”

Section: Discussionmentioning

confidence: 99%

The role of scale in designing protected area systems to conserve poorly known species

2015

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Possingham. 2015. The role of scale in designing protected area systems to conserve poorly known species. Ecosphere 6(11):237. http://dx.doi.org/10.1890/ES15-00346.1Abstract. Systematic conservation planning has a substantial theoretical underpinning that allows optimization of tradeoffs between biodiversity conservation and other socioeconomic goals. However, this theory assumes perfect spatial information about the locations of biodiversity features (e.g., species distributions). In practice, planners represent well-known taxa and other biodiversity ''surrogates'' in protected area systems, hoping that unmapped species will also be conserved. However, empirical research finds that surrogates predict species presence imperfectly, and sometimes rather poorly, at scales relevant to planning, and existing theory provides no further guidance. We developed new theory, explicitly incorporating aspects of spatial scale, for the representation problem when the locations of species distributions are unknown. Using probability theory and simulated and real species distributions, we found that the probability of adequately representing an unmapped species in a protected area system will be low unless the total fraction of the region being protected is larger than the species representation target. Furthermore, successful conservation depended critically on the relative sizes of the species distribution and of the individual protected areas; fewer, larger protected areas allowed the entire species distribution to fall into an unprotected gap. This scale-dependence varied with the configuration of the protected area system, with the conservation objective most likely to be attained if the individual protected areas were hyperdispersed (evenly spaced across the planning region). Using these results, we developed three design principles for representing unmapped species in protected areas: (1) The fraction of the region placed in protected areas should be substantially larger than the species-level representation target; (2) Individual protected areas must be at least one to two orders of magnitude smaller than the unmapped species' distribution; and (3) Protected areas should be evenly dispersed over geographic space. We also performed preliminary investigations of the effects of surrogates and socio-economic cost data on the probability of adequately representing unmapped species, finding that the primary effect of surrogates may simply be to promote hyperdispersion of protected areas across the planning region, and that seeking to minimize opportunity costs gives poorer conservation results than random protected area placement.

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“…Indeed a common problem is associated with data on the absence of species (Lobo, 2007;Jimenez-Valverde and Lobo, 2007;Graham et al, 2008). Fundamentally, it is normally impossible to be confident that a recorded absence is actually nothing more than an undetected presence (MacKenzie, 2005;Cronin and Vickers, 2008;Franklin et al, 2009). False absence cases may arise for a variety of reasons and may occur especially for cryptic species that are of difficult to detect.…”

Section: Accuracy and Comparisonmentioning

confidence: 99%

“…The latter typically involves the comparison of the derived product with reality and calculation of summary quality measures. Unfortunately, reality or the "truth" about the feature under study is rarely known unless a simulated data set is used Carlotto, 2009;Foody, 2009a;Franklin et al, 2009). For example, errors in ground data sets used in remote sensing of land cover may be large (Foody, 2009a).…”

Section: Accuracy and Comparisonmentioning

confidence: 99%

An overview of recent remote sensing and GIS based research in ecological informatics

Boyd

Foody

2011

Ecological Informatics

118

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This article provides an overview of some of the recent research in ecological informatics involving remote sensing and GIS. Attention focuses on a selected range of issues including topics such as the nature of remote sensing data sets, issues of accuracy and uncertainty, data visualization and sharing activities as well as developments in aspects of ecological modeling research. It is shown that considerable advances have been made over recent years and foundations for future research established. BackgroundGeotechnology has been couched as one of the three "mega-technologies" of the 21 st century, along with nanotechnology and biotechnology (Gewin, 2004). On the cusp of the second decade of the 21 st century it is evident that recent developments in the geotechnologies of Geographic Information Science (GIS) and remote sensing have had a substantial impact on ecological research, providing spatial data and associated information to enable the further understanding of ecological systems (Rundell et al., 2009). In a digital society there is sometimes an expectation that information is just a click away and this may apply to spatial information in the domain of ecological informatics. Indeed, it has been in the last five years that developments in and relating to the fields of GIS and remote sensing, such as those seen in internet technologies (e.g., Web 2.0; high performance communication networks; Brunt et al., 2007), sensing technology (e.g., quantum well infrared photodetectors; Krabach, 2000), data standards and interoperability (e.g., OGC® KML 2.2) and spatially explicit modeling (e.g., Osborne et al., 2007), have resulted in the revelation of previously unobservable phenomena and posing of what might be termed second generation of ecological questions. During this time we have also seen more support for the open exchange of data and software and infrastructure for location based services. This paper serves as a review of some of the recent developments in GIS and remote sensing that have been or promises to be of benefit to ecological informatics. The subjects addressed are necessarily selective and limited in scope, drawing on topics of interest to us and, we hope, to you.

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Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

Cited by 119 publications

References 67 publications

An evaluation of environmental factors affecting species distributions

An evaluation of environmental factors affecting species distributions

The role of scale in designing protected area systems to conserve poorly known species

An overview of recent remote sensing and GIS based research in ecological informatics

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