Importance and effectiveness of correction methods for spatial sampling bias in species with sex‐specific habitat preference

Abstract

AimPresence records from surveys with spatially heterogeneous sampling intensity are a key challenge for species distribution models (SDMs). When sex groups differ in their habitat association, the correction of the spatial bias becomes important for preventing model predictions that are biased toward one sex. The objectives of this study were to investigate the effectiveness of existing correction methods for spatial sampling bias for SDMs when male and female have different habitat preferences.LocationJura massif, France.MethodsWe used a spatially sex‐segregated virtual species to understand the effect of three sampling designs (spatially biased, uniform random, and systematic), and two correction methods (targeted background points, and distance to trajectories) on estimated habitat preferences, sex ratios, and prediction accuracy. We then evaluated these effects for two empirical Capercaillie (Tetrao urogallus) presence‐only datasets from a systematic and a spatially biased sampling design.ResultsSampling design strongly affected parameter estimation accuracy for the virtual species: noncorrected spatially biased sampling resulted in biased estimates of habitat association and sex ratios. Both established methods of bias correction were successful in the case of virtual species, with the targeted correction methods showing stronger correction, as it more closely followed the simulated decay of detectability with distance from sampling locations. On the Capercaillie dataset, only the targeted background points method resulted in the same sex ratio estimate for the spatially biased sampling design as for the spatially unbiased sampling.Main conclusionsWe suggest that information on subgroups with distinct habitat associations should be included in SDMs analyses when possible. We conclude that current methods for correcting spatially biased sampling can improve estimates of both habitat association and subgroup ratios (e.g., sex and age), but that their efficiency depends on their ability to well represent the spatial observation bias.

Top predator status and trends: ecological implications, monitoring and mitigation strategies to promote ecosystem-based management

“…Under specific conditions, data collected from platforms of opportunity (e.g., from ferries: Robbins et al, 2020; cargo ships, fishing vessels: Louzao et al, 2020; or whale watching: Peŕez-Jorge et al, 2016) may be used to detect relative trends and complement the knowledge, e.g., on species presence. However, the lack of a systematic data collection approach can drive biases and low predictive power (e.g., Glad et al, 2019).…”

An atlas of seabed biodiversity for Aotearoa New Zealand

“…Geographic information systems (GIS) provide a tool to store, visualise and explore relationships between spatial information describing taxa distributions (Hamylton, 2017). Increasingly GIS are available online (as online atlases, e.g., (Asaad et al, 2019), providing a platform to explore and download data without the requirement to have access to traditional desktop-based 60 GIS.…”

“…Increasingly GIS are available online (as online atlases, e.g., (Asaad et al, 2019), providing a platform to explore and download data without the requirement to have access to traditional desktop-based 60 GIS. Online atlases exist for several coastal regions, including Ireland's Marine Atlas (https://atlas.marine.ie/), the Oregon Coastal Atlas (https://www.coastalatlas.net/), the European Atlas of the Seas (Barale et al, 2015) 57°S; 162°E -172°W) and contains high biological diversity, reflecting its wide latitudinal gradient (subtropical to subantarctic), range of water depths (intertidal to deep ocean trenches), and zones of high productivity (Costello et al, 2009, Gordon et al, 2010. In addition, the relatively large distance between Aotearoa New Zealand and other major land masses 70 has resulted in a high degree of species endemism (i.e., a high number of species are not found elsewhere on the planet) (Gordon et al, 2010).…”

“…Online atlases exist for several coastal regions, including Ireland's Marine Atlas (https://atlas.marine.ie/), the Oregon Coastal Atlas (https://www.coastalatlas.net/), the European Atlas of the Seas (Barale et al, 2015) 57°S; 162°E -172°W) and contains high biological diversity, reflecting its wide latitudinal gradient (subtropical to subantarctic), range of water depths (intertidal to deep ocean trenches), and zones of high productivity (Costello et al, 2009, Gordon et al, 2010. In addition, the relatively large distance between Aotearoa New Zealand and other major land masses 70 has resulted in a high degree of species endemism (i.e., a high number of species are not found elsewhere on the planet) (Gordon et al, 2010). Such high biodiversity values mean that it is critical that spatial information of biodiversity is available for effective spatial management and conservation planning (Costello et al, 2017).…”

Correlations between spatial sampling biases and environmental niches affect species distribution models

“…Another approach commonly applied to presence‐only data, where in lieu of absence information a large number of background points are used to provide a sample of the environmental conditions available in a landscape, is to reduce the influence of sampling bias on model predictions by altering the background sample, often by sampling background points to have a similar bias to the presence data (Phillips et al., 2009; Warren et al., 2014). Although these approaches have been shown to improve model predictive performance in many cases, considerable variation has been documented, associated variously with species types (e.g., rare or common, generalist or specialist, sex‐specific habitat preference), data quality (e.g., number of species records, degree of spatial bias) and bias‐correction method (Dubos et al., 2021; El‐Gabbas et al, 2018; Glad et al., 2019; Inman et al., 2021; Kramer‐Schadt et al., 2013; Ranc et al., 2017). Common to different bias‐correction methods is the challenge of correctly inferring the pattern or drivers of bias in the occurrence data and, therefore, it is perhaps not surprising that the effects of spatial sampling biases are not consistent or easily addressed, because the biases themselves arise as a result of complex interactions between human behaviour, geography and species characteristics and can be driven by processes operating at different spatial scales.…”