Using single visits into integrated occupancy models to make the most of existing monitoring programs

Abstract

A major challenge in statistical ecology consists of integrating knowledge from different data sets to produce robust ecological indicators. To estimate species distribution, occupancy models are a flexible framework that can accommodate several data sets obtained from different sampling methods. However, repeating visits at sampling sites is a prerequisite for using standard occupancy models. Occupancy models were recently developed to analyze detection/ non-detection data collected during a single visit. To date, single-visit occupancy models have never been used to integrate several different data sets. Here, we showcase an approach that combines two data sets into an integrated single-visit occupancy model. As a case study, we estimated the distribution of common bottlenose dolphin (Tursiops truncatus) over the northwestern Mediterranean Sea by combining 24,624 km of aerial surveys and 21,464 km of at-sea monitoring. We compared the outputs of single-vs. repeated-visit occupancy models into integrated occupancy models. Integrated models allowed a better sampling coverage of the targeted population, which provided a better precision for occupancy estimates than occupancy models using data sets in isolation. Overall, single-and repeated-visit integrated occupancy models produced similar inference about the distribution of bottlenose dolphins. We suggest that single-visit occupancy models open promising perspectives for the use of existing ecological data sets.

Data Gap or Biodiversity Gap? Evaluating apparent spatial biases in community science observations of Odonata in the east-central United States

“…Our findings agreed with numerous studies supporting the viability of community science in conservation (Lauret et al, 2021; McKinley et al, 2017; Walker et al, 2016) and Odonata biodiversity research (Patten et al, 2019; Rapacciuolo et al, 2017b). However, while inconclusive and of limited spatiotemporal scale, our observations of the data gap region mirror concerns about sampling biases and decision-making in community science that have also been the focus of many other studies (Archer et al, 2014; Bowler et al, 2022; Johnston et al, 2020; Millar et al, 2019; Ruete, 2015).…”

“…Community science initiatives have been crucial for understanding changes in biodiversity, distribution, and phenology, due to their potential to generate large volumes of data and cover broad geographical areas (Fraisl et al, 2022). The possible benefits of community science have been well-documented and could represent a viable alternative to data acquisition for projects where scarce financial or logistical resources prevent traditional, multi-visit sampling (Lauret et al, 2021). For example, in one study, youth volunteers were found to have observed proportionally higher numbers of mollusks, arachnids, and insects than the average iNaturalist user (Aristeidou et al, 2021).…”

Integrated spatial models foster complementarity between monitoring programs in producing large-scale bottlenose dolphin indicators

“…In parallel, the French office for biodiversity develops and supports local monitoring programs in the French MPA network to perform photo-id data continuously, such detailed datasets represent an important asset to inform abundance of marine mammals populations (Evans & Hammond, 2004). Ecological indicators required by the Marine Strategy Framework Directive for bottlenose dolphins would benefit from integrating aerial line-transect with more data when available (Lauret et al 2021). In addition, the French Research Institute for Exploitation of the Sea (i.e.…”

“…Framework Directive for bottlenose dolphins would benefit from integrating aerial linetransect with more data when available (Lauret et al 2021). In addition, the French Research Institute for Exploitation of the Sea (i.e.…”

“…The multiplication of monitoring programs over the same conservation context has fostered the development of statistical models that can estimate ecological indicators while accommodating several, possibly heterogeneous, datasets (Besbeas et al, 2002; Miller et al, 2019; Isaac et al, 2019; Zipkin, Inouye, & Beissinger, 2019; Farr et al, 2020). Integrating data from several monitoring protocols can give complementary insights on population structure and dynamics (Schaub & Abadi, 2011), increase space and time coverage of the population (Schaub & Abadi, 2011; Zipkin, Inouye, & Beissinger, 2019), and produce more precise estimates of ecological indicators (Isaac et al 2019; Lauret et al 2021; Farr et al 2020).…”