Predicting species distributions based on incomplete survey data: the trade‐off between precision and scale

Abstract

Systematic species surveys over large areas are mostly not affordable, constraining conservation planners to make best use of incomplete data. Spatially explicit species distribution models (SDM) may be useful to detect and compensate for incomplete information. SDMs can either be based on standardized, systematic sampling in a restricted subarea, or – as a cost‐effective alternative – on data haphazardly collated by “volunteer‐based monitoring schemes” (VMS), area‐wide but inherently biased and of heterogeneous spatial precision. Using data on capercaillie Tetrao urogallus, we evaluated the capacity of SDMs generated from incomplete survey data to localise unknown areas inhabited by the species and to predict relative local observation density. Addressing the trade‐off between data precision, sample size and spatial extent of the sampling area, we compared three different sampling strategies: VMS‐data collected throughout the whole study area (7000 km2) using either 1) exact locations or 2) locations aggregated to grid cells of the size of an average individual home range, and 3) systematic transect counts conducted within a small subarea (23.8 km2). For each strategy, we compared two sample sizes and two modelling methods (ENFA and Maxent), which were evaluated using cross‐validation and independent data. Models based on VMS‐data (strategies 1 and 2) performed equally well in predicting relative observation density and in localizing “unknown” occurrences. They always outperformed strategy 3‐models, irrespective of sample size and modelling method, partly because the VMS‐data provided the more comprehensive clues for setting the discrimination‐threshold for predicting presence or absence. Accounting for potential errors due to extrapolation (e.g. projections outside the environmental domain or potentially biasing variables) reduced, but did not fully compensate for the observed discrepancies. As they cover a broader range of species‐habitat relations, the area‐wide data achieved a better model quality with less a‐priori knowledge. Furthermore, in a highly mobile species like capercaillie a sampling resolution corresponding to an individuals' home range can lead to equally good predictions as the use of exact locations. Consequently, when a trade‐off between the sampling effort and the spatial extent of the sampling area is necessary, less precise data unsystematically collected over a large representative region are preferable to systematically sampled data from a restricted region.

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

“…These elements have to be acknowledged and appropriately dealt with as they are inherent to map production and may cause issues for conservation and management planning (Tulloch et al, 2016). In addition, they are all related; for instance, there is often a relationship between data quality and spatial scale (Braunisch and Suchant, 2010;, and between data quality and model selection (Rondinini et al, 2006). This contribution to the literature was written keeping this antinomy in mind, which can be summarized with a quote from Box and Draper (1987), who wrote that "essentially, all models are wrong, but some are useful, " and a subsequent quote by : "all spatial data are wrong, but some are useful."…”

Detecting the potential sympatric range and niche divergence between Asian endemic ungulates of Procapra

“…To strike a balance between the spatial resolution of environmental variables and occurrence data, and also to consider the effects of reduced resolution on predicted outputs (Braunisch and Suchant 2010;Hu and Jiang 2010), we resampled all variables with an initial grid cell of 1×1 km to 8×8 km using ArcGIS 9.2 (ESRI, Redland, USA).…”

“…Compared with spatial resolution, which has attracted substantial attention (e.g., Braunisch and Suchant 2010;Hu and Jiang 2010), the extent of the study region has until recently been ignored, or at least has not been considered explicitly in most studies (e.g., Costa and Schlupp 2010; but see Acevedo et al 2012;Anderson and Raza 2010;Barve et al 2011). Because patterns observed on one scale may not be apparent on another, it is essential to understand the theory and processes driving the observed distribution patterns in order to avoid a mismatch between the scale used for modeling and the one at which key processes occur (Guisan and Thuiller 2005).…”

Spatial scale and geographic context in benthic habitat mapping: review and future directions

“…Despite attempts to address this challenge (e.g. Braunisch & Suchant 2010), it is still unclear which characteristics should be given a higher priority in sampling strategy. Fine reso lution data arguably yields better predictive models if the data quality is adequate, even if the sample size of biological data is smaller (Huston 2002, Engler et al 2004, Kaliontzopoulou et al 2008, Reside et al 2011, but this conclusion is not unanimous (Braunisch & Suchant 2010).…”