Using animal movement behavior to categorize land cover and predict consequences for connectivity and patch residence times

Brown, Leone M.; Fuda, Rebecca K.; Schtickzelle, Nicolas; Coffman, Haley; Jost, Audrey; Kazberouk, Alice; Kemper, Eliot; Sass, Emma M.; Crone, Elizabeth E.

doi:10.1007/s10980-017-0533-8

Cited by 31 publications

(41 citation statements)

References 78 publications

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“…At forest edges, butterflies crossed into forest habitat with binomial probability 0.3, which is somewhat more often than observed for upland prairie butterflies (Schultz et al 2012, Brown et al 2017, but is less than wet meadow butterflies at forest edges (Kuefler et al 2010). At forest edges, butterflies crossed into forest habitat with binomial probability 0.3, which is somewhat more often than observed for upland prairie butterflies (Schultz et al 2012, Brown et al 2017, but is less than wet meadow butterflies at forest edges (Kuefler et al 2010).…”

Section: Landscapementioning

confidence: 88%

“…Because we were not able to conduct extensive field studies of the federally listed Taylor's checkerspot, we use previously published parameters from a surrogate species, Baltimore checkerspot (Brown et al 2017) in our simulations. Baltimore checkerspots have similar life cycles, and although they use mesic meadows and Taylor's checkerspots use drier upland prairie (Severns and Breed 2014), the two species are close phylogenetically (Wahlberg et al 2005, Long et al 2014) and behaviorally.…”

Section: Life Cycle Of Taylor's Checkerspot Butterfliesmentioning

confidence: 99%

“…Root 1973), but if those high numbers cause overexploitation of host plants, the population could crash (endogenous dynamics). To assess whether the optimal planting strategy would change depending on the mechanism generating population fluctuations, we constructed two spatially explicit individual-based simulation models (SEIBMs), parameterized them with demographic and movement data from a surrogate species, the Baltimore checkerspot (Brown et al 2017), and ran them on a landscape representative of actual Taylor's checkerspot restoration sites in Washington state, USA. However, the extinction risk of a patch is expected to increase as its size decreases and the strength of demographic stochasticity increases (Diamond 1975, Schoener and Schoener 1983, Hokit and Branch 2003.…”

Section: Introductionmentioning

confidence: 99%

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Mechanism matters: the cause of fluctuations in boom–bust populations governs optimal habitat restoration strategy

Boor

Schultz

Crone

et al. 2018

Ecological Applications

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Many populations exhibit boom-bust dynamics in which abundance fluctuates dramatically over time. Past research has focused on identifying whether the cause of fluctuations is primarily exogenous, e.g., environmental stochasticity coupled with weak density dependence, or endogenous, e.g., over-compensatory density dependence. Far fewer studies have addressed whether the mechanism responsible for boom-bust dynamics matters with respect to at-risk species management. Here, we ask whether the best strategy for restoring habitat across a landscape differs under exogenously vs. endogenously driven boom-bust dynamics. We used spatially explicit individual-based models to assess how butterfly populations governed by the two mechanisms would respond to habitat restoration strategies that varied in the level of resource patchiness, from a single large patch to multiple patches spaced at different distances. Our models showed that the restoration strategy that minimized extinction risk and boom-bust dynamics would be markedly different depending on the governing mechanism. Exogenously governed populations fared best in a single large habitat patch, whereas for endogenously driven populations, boom-bust dynamics were dampened and extinction risk declined when the total restored area was split into multiple patches with low to moderate inter-patch spacing. Adding environmental stochasticity to the endogenous model did not alter this result. Habitat fragmentation lowered extinction risk in the endogenously driven populations by reducing their growth rate, precluding both "boom" phases and, more importantly, "bust" phases. Our findings suggest that (1) successful restoration will depend on understanding the causes of fluctuations in at-risk populations, (2) the level and pattern of spatiotemporal environmental heterogeneity will also affect the ideal management approach, and (3) counterintuitively, for at-risk species with endogenously governed boom-bust dynamics, lowering the intrinsic population growth rate may decrease extinction risk.

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Section: Landscapementioning

confidence: 88%

Section: Life Cycle Of Taylor's Checkerspot Butterfliesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mechanism matters: the cause of fluctuations in boom–bust populations governs optimal habitat restoration strategy

Boor

Schultz

Crone

et al. 2018

Ecological Applications

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“…Drawing from diffusion theory, which predicts lower population densities in land cover that facilitates movement (Schultz et al 2017), it seems that in our system impervious cover and associated features of the urban environment favor site infidelity of individuals to the garden system. Many (possibly most) organisms move faster in the landscape matrix than in habitat patches (Kareiva and Odell 1987;Schultz 1998;Brown et al 2017;Lutscher and Musgrave 2017), attributed in part to edge effects. In low quality cityscapes with greater impervious habitat, individuals are more likely to come upon an edge, thereby triggering long range movement to the next high quality patch.…”

Section: Discussionmentioning

confidence: 99%

Cityscape quality and resource manipulation affect natural enemy biodiversity in and fidelity to urban agroecosystems

et al. 2018

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Context Complex landscapes with high resource availability can support more diverse natural enemy communities and better natural pest control by providing resources and facilitating organism dispersal. Moreover, in agricultural landscapes, local agroecosystem management can support biodiversity maintenance and pest control by adding resources in less complex landscapes with fewer resources. However, we lack an understanding of how local and landscape factors interact to affect natural enemy communities and their site fidelity to agroecosystems in urban landscapes (i.e., cityscapes). Objective To better understand how local and landscape factors influence natural enemies in urban agroecosystems, we used urban community gardens as a model system to test if and how local resource manipulation and differences in cityscape quality affect natural enemy (ladybird beetles, parasitoid wasps) communities and their fidelity to urban habitats. Methods We performed two manipulations. First, we added local floral resources in 6 of 12 gardens situated in different cityscapes to measure differences in natural enemy biodiversity. Second, in those 12 gardens, with and without resource additions, we manipulated populations of a common natural enemy, Hippodamia convergens, to assess fidelity to the gardens. Results Floral resource additions increased parasitoid abundance and changed community composition, but had little effect on ladybeetle abundance, richness or site fidelity. Rather, ladybeetle fidelity to gardens was lower in gardens in low quality cityscapes with high impervious cover. Conclusions Cityscape quality influences natural enemies in and fidelity to gardens. Landscape-moderated biodiversity patterns observed in rural landscapes likely differ from urban contexts with implications for pest control.

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“…; Brown et al. ; Rickbeil et al. ), this premise has not been tested for the purpose of land cover mapping.…”

Section: Introductionmentioning

confidence: 99%

From ecology to remote sensing: using animals to map land cover

Remelgado

Safi

Wegmann³

2019

Remote Sens Ecol Conserv

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Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence–absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high‐resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1‐scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non‐irrigated land. When both options occurred, the animals disregarded non‐irrigated land leading to its misclassification as non‐agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non‐irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture – with other land‐use dependencies and observation times – can contribute to better results in similar applications.

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Using animal movement behavior to categorize land cover and predict consequences for connectivity and patch residence times

Cited by 31 publications

References 78 publications

Mechanism matters: the cause of fluctuations in boom–bust populations governs optimal habitat restoration strategy

Mechanism matters: the cause of fluctuations in boom–bust populations governs optimal habitat restoration strategy

Cityscape quality and resource manipulation affect natural enemy biodiversity in and fidelity to urban agroecosystems

From ecology to remote sensing: using animals to map land cover

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