rsmove—An r package to bridge remote sensing and movement ecology

Abstract

Remote sensing is a valuable tool in movement ecology. However, the way it is combined with animal movement data is far from optimal as most studies overlook differences in the spatial, temporal and thematic resolutions between both data types. rsmove uses a pixel‐based approach to link animal tracking and remote sensing data that bridge the gap between these two disciplines while respecting the limitations of the latest. Additionally, if offers standardize methods to pre‐analyse the connection between animal movement and environmental change. The package guides the choice of study sites, satellite data and environmental predictors though data mining and visualization tools. rsmove offers a simple methodology to analyse animal–environment interactions that helps avoid unnecessary, time‐consuming satellite data processing through an informed decision process. The package offers a basis for a better communication between ecologists and remote sensing experts contributing to the definition of clear remote sensing data requirements. Additionally, it provides future studies on animal movement tools for a more critical approach on the selection of environmental data supporting the reproducibility of animal movement studies.

A methodological roadmap to quantify animal‐vectored spatial ecosystem subsidies

“…These are defined as the time it takes an animal to cross a circle with a defined radius --and as such scale dependent --and can quantify the time duration of an individual animal present within such a circle (Fauchald & Tveraa 2003) (Johnson 1980;Mertes & Jetz 2017;Mertes et al 2020) should drive the spatial resolution of remote sensing products selected for analysis, not the other way around. This is especially relevant for animal movement data, which are typically measured at finer spatio-temporal resolutions than data from remotely sensed imagery (Remelgado et al 2017(Remelgado et al , 2019. The habitat domain can be measured using movement data by tracked individuals across a landscape, to calculate an animals utilization distribution and probabilities of spatial locations associated with foraging and migration behaviour across a landscape (Schmitz et al 2017).…”

Optimizing the use of biologgers for movement ecology research

“…Thus, a major aspiration is to link such data to movement. Though, while ecologists can access an increasing amount of remotely sensed environmental data, linking them to location data is usually difficult, as environmental data are obtained at different, generally coarser, spatiotemporal scales than movement data(Dodge et al, 2013) Remelgado, Wegmann, and Safi (2019). recently developed a new pixel-based approach, combined with data mining and visualization, to help ecologists efficiently deal with differences in the spatial, temporal and thematic resolutions between environmental data from remote sensing and GPS location data; yet the problem persists with high-frequency biologging data.Depending on the question asked, it may be necessary to use…”

A checklist for choosing between R packages in ecology and evolution

“…Furthermore, publications describing the use of packages in ecology and evolution strongly suggest that the package will be functional and potentially appropriate to your own adventure, that is, see the R package codyn on CRAN for community dynamic measures with documentation and a peer‐reviewed publication to describe it in depth (Hallett et al, ). There are many other examples also published in the journal Methods in Ecology and Evolution (Duthie et al, ; Harmer & Thomas, ; Remelgado, Wegmann, & Safi, ; Wubs et al, ). Another important attribute of packages is the frequency of use.…”

“…Methods in Ecology and Evolution(Duthie et al, 2018;Harmer & Thomas, 2019;Remelgado, Wegmann, & Safi, 2019;Wubs et al, 2019). Another important attribute of packages is the frequency of use.…”