Hierarchical multi‐grain models improve descriptions of species’ environmental associations, distribution, and abundance

Abstract

The characterization of species’ environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e., grain) of analysis. Ecological theory and empirical evidence suggest that range‐resident species respond to their environment at two characteristic, hierarchical spatial grains: (1) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (2) occupancy grain, the (relatively coarse) grain equivalent to a typical home range. We use a multi‐grain (MG) occupancy model, aided by fine‐grain remotely sensed imagery, to simultaneously estimate species–environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium‐sized bird (Tockus deckeni) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1 km for T. deckeni. Using a grain optimization procedure across 32 grains from 10 to 500 m, we identify 60 m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single‐grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi‐scale structure of species’ environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population size in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates, and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi‐grain approaches of the sort presented, and increasingly enabled by growing high‐resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species’ distributions.

A methodological roadmap to quantify animal‐vectored spatial ecosystem subsidies

“…responses to environmental resources such as foraging [see section 3]) or at coarser scales, such as their broad home range (introduced in section 4) (Mertes et al 2020). Fine and coarse spatial grains have been termed "response grain" and "occupancy grains", respectively (Mertes et al 2020). To quantify an animal's response grain, first passage time analysis can be employed.…”

“…To quantify an animal's response grain, first passage time analysis can be employed. 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.…”

“…Biologging enables quantification of the space-use and resource requirements of animals (Kays et al 2015;Hays et al 2019). The frequency with which animals visit certain areas (e.g., waterholes, fruit bearing tree, latrines) can be estimated via first passage times and recursive analysis (Mahoney & Young 2017;Bracis et al 2018;Mertes et al 2020).…”

Nested Species Distribution Models of Chlamydiales in Ixodes ricinus (Tick) Hosts in Switzerland

“…This should be favour over using an average of the climatic conditions over the sampling period (36,96) or any larger period of time (as Worldclim climatic data from 1950 to 2000 which are commonly used for species distribution modelling (97, 98)). Previous studies already suggested the use of multi-grain approaches involving various spatial resolutions to consider variables affecting the presence of a species at different scales (99)(100)(101). This adds to the recommendation of using data based on species on November 2, 2020 by guest http://aem.asm.org/ Downloaded from ecology rather than on availability (100,102).…”

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.…”