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Jetz, Walter

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Jetz
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    Data from: Individual environmental niches in mobile organisms
    (2021-02-19) Carlson, Ben; Rotics, Shay; Nathan, Ran; Wikelski, Martin; Jetz, Walter
    Individual variation is increasingly recognized as a central component of ecological processes, but its role in structuring environmental niche associations remains largely unknown. Species’ responses to environmental conditions are ultimately determined by the niches of single individuals, yet environmental associations are typically captured only at the level of species. Here, we develop scenarios for how individual variation may combine to define the compound environmental niche of populations, use extensive movement data to document individual environmental niche variation, test associated hypotheses of niche configuration, and examine the consistency of individual niches over time. For 45 individual white storks (Ciconia ciconia; 116 individual-year combinations), we uncover high variability in individual environmental associations, consistency of individual niches over time, and moderate to strong niche specialization. Within populations, environmental niches follow a nested pattern, with individuals arranged along a specialist-to-generalist gradient. These results reject common assumptions of individual niche equivalency among conspecifics, as well as the separation of individual niches into disparate parts of environmental space. These findings underscore the need for a more thorough consideration of individualistic environmental responses in global change research.
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    Data from: Hierarchical multi-grain models improve descriptions of species’ environmental associations, distribution, and abundance
    (2020-03-18) Mertes, Katherine; Jetz, Walter; Wikelski, Martin
    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: (i) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (ii) 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 1km for T. deckeni. Using a grain optimization procedure across 32 grains from 10m to 500m, we identify 60m 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 sizes 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.