Assumption‐versus data‐based approaches to summarizing species’ ranges

Abstract

For conservation decision making, species' geographic distributions are mapped using various approaches. Some such efforts have downscaled versions of coarse-resolution extent-of-occurrence maps to fine resolutions for conservation planning. We examined the quality of the extent-of-occurrence maps as range summaries and the utility of refining those maps into fine-resolution distributional hypotheses. Extent-of-occurrence maps tend to be overly simple, omit many known and well-documented populations, and likely frequently include many areas not holding populations. Refinement steps involve typological assumptions about habitat preferences and elevational ranges of species, which can introduce substantial error in estimates of species' true areas of distribution. However, no model-evaluation steps are taken to assess the predictive ability of these models, so model inaccuracies are not noticed. Whereas range summaries derived by these methods may be useful in coarse-grained, global-extent studies, their continued use in on-the-ground conservation applications at fine spatial resolutions is not advisable in light of reliance on assumptions, lack of real spatial resolution, and lack of testing. In contrast, data-driven techniques that integrate primary data on biodiversity occurrence with remotely sensed data that summarize environmental dimensions (i.e., ecological niche modeling or species distribution modeling) offer data-driven solutions based on a minimum of assumptions that can be evaluated and validated quantitatively to offer a well-founded, widely accepted method for summarizing species' distributional patterns for conservation applications.

Avian SDMs: current state, challenges, and opportunities

“…While at coarse continental scales, limits of bird distributions are relatively well known, more detailed, fine-grained information on range limits is lacking for many species (Lawler et al 2011, Peterson et al 2016. Many studies have used SDMs to identify critical areas for conservation, complement established reserve networks, consider data deficient species, or meet specific conservation targets (Seavy et al 2012; see also Table 1 for more details and examples).…”

Hotspot prioritizations show sensitivity to data type

“…It is therefore important to explore approaches for using currently available survey and range data as effectively as possible. Developments in single species and joint species distribution modeling offer promising approaches for filling geographic and temporal gaps in survey data using environmental information 27,28 . Spatial interpolation has been shown to perform well for smoothing spatial gaps in survey data 29 .…”

A framework integrating physiology, dispersal and land‐use to project species ranges under climate change

“…For this purpose, we used a global digital elevation model (GTOPO30, 30 arc seconds) provided by the US Geological Survey (< https:// earthexplorer.usgs.gov/ >), which we aggregated to the used spatial resolution of 0.5°. We would like to note that such coarse outlines of species range maps should be utilized and interpreted with care, due to their large commission (but also omission) errors (Peterson et al 2016). While their use is especially problematic for informing local-scale conservation planning (Peterson et al 2016), they serve their purpose in large-and coarse-scale investigations of species distributions and diversity (but see Eyres et al 2017 for a comparison of diversity and distribution patterns based on different data types).…”

“…We would like to note that such coarse outlines of species range maps should be utilized and interpreted with care, due to their large commission (but also omission) errors (Peterson et al 2016). While their use is especially problematic for informing local-scale conservation planning (Peterson et al 2016), they serve their purpose in large-and coarse-scale investigations of species distributions and diversity (but see Eyres et al 2017 for a comparison of diversity and distribution patterns based on different data types). For our analyses, we emphasize that instead of providing detailed species-and region-specific predictions of the future occurrence of E. hortulana, our aim is to illustrate a new framework for the integration of species-specific information on physiological and dispersal capacities with different aspects of global change (climate and land-use change) at large spatial scales.…”