A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps

Tehrani, Nasrin Amini; Naimi, Babak; Jaboyedoff, Michel

doi:10.1016/j.ecoinf.2021.101501

Cited by 4 publications

(1 citation statement)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies commonly rely on datasets compiled from many sources and are composed of observations collected using different sampling methodologies. Fitting models to aggregate or subset data can result in a loss of information (Tehrani et al., 2021), and lead to biased parameter estimation and underestimates of uncertainty (Calabrese et al., 2014). Multispecies studies that span larger spatial extents and multiple systems are particularly impacted by these ad hoc approaches given the aforementioned factors affecting catchability.…”

Section: Introductionmentioning

confidence: 99%

Accounting for spatiotemporal sampling variation in joint species distribution models

North,

Schliep,

Hansen

et al. 2023

Journal of Applied Ecology

View full text Add to dashboard Cite

Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

show abstract

Section: Introductionmentioning

confidence: 99%

Accounting for spatiotemporal sampling variation in joint species distribution models

North,

Schliep,

Hansen

et al. 2023

Journal of Applied Ecology

View full text Add to dashboard Cite

show abstract

Wintering raptor species distribution in a semiarid Mediterranean region: the relevance of lowlands and open habitats as stopover sites

Álvarez,

León-Ortega,

Martínez

et al. 2024

Eur J Wildl Res

View full text Add to dashboard Cite

As land-use cover types affect wintering birds’ strategies, studying their habitat suitability for species conservation is relevant. Predictive spatial models are considered excellent tools for conservation planning and improving our understanding of species distribution. Here we build models to predict the spatial distribution of raptor species that overwinter in SE Spain. We modelled the wintering raptor species distribution based on the presence-only data obtained between 2017 and 2019 and analysed their habitat preferences based on elevation and land-use cover variables. Our results show that altitude and distance to the coastline are the most important environmental factors to affect most species’ habitat suitability. Habitat type-related factors are other important predictors, and raptors subject of this study prefer lowlands and areas close to the coastline for overwintering by selecting open habitats, which include wetlands and irrigated and rainfed crops. This study highlights the importance of the habitat heterogeneity generated by wetlands and herbaceous crops within a low-altitude range as the optimal environment for wintering raptors. Such information should be considered for raptor conservation planning in human-altered landscapes.

show abstract

Species density per grid cell no longer predicts the local abundance of woody plants

Maciel

Guilherme

2022

Ecological Informatics

View full text Add to dashboard Cite

A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps

Cited by 4 publications

References 94 publications

Accounting for spatiotemporal sampling variation in joint species distribution models

Accounting for spatiotemporal sampling variation in joint species distribution models

Wintering raptor species distribution in a semiarid Mediterranean region: the relevance of lowlands and open habitats as stopover sites

Species density per grid cell no longer predicts the local abundance of woody plants

Contact Info

Product

Resources

About