A spatially explicit, multi‐scale occupancy model for large‐scale population monitoring

Crosby, Andrew D.; Porter, William F.

doi:10.1002/jwmg.21466

Cited by 5 publications

(9 citation statements)

References 48 publications

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“…Surveys occurred 1–3 days after a snowfall event, and the probability

δ_{j}

of observable tracks being laid was a function of time elapsed since the last snowfall event, and was also a Markovian process where track‐laying was more likely at segment i if tracks were also laid at segment i − 1 (Crosby and Porter 2018). For the purpose of simulating data, we defined

ψ_{i}

and

θ_{ij}

as in Crosby and Porter (2018);

ψ_{i}

was the probability that a transect passed through ≥1 home range, and

θ_{ij} = P {(u}_{ij} = 1)

was the probability that route segment j on route i intersected ≥1 home range. In our simulated data,

u_{ij} = 1

when a segment j in sample unit i intersected a home range.…”

Section: Methodsmentioning

confidence: 99%

“…If defined appropriately, road segments may approximate point sampling units and possibly mitigate biases expected with areal sampling units (Efford and Dawson 2012). We conducted a series of simulations where we used the occupancy model of Crosby and Porter (2018) to generate data from simulated survey routes, and we fitted the simulated data to several models that used either areal grid cells or 1‐km route segments (to approximate points) as sample units (Table 1). The Crosby and Porter (2018) model explicitly separates the probability of track‐laying

δ

from the probability of occupancy (or use), and models the former as a function of the number of days elapsed since the last snowfall event, assuming that track‐laying accumulates, and tracks persist over time.…”

Section: Methodsmentioning

confidence: 99%

“…, where ρ ij was given an intrinsic CAR prior, with weights equal to 1 for neighbors and 0 otherwise (Crosby and Porter 2018). Detection probability was high (p = 0.85), given = u 1 ij .…”

Section: Modelmentioning

confidence: 99%

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Scaling Occupancy Estimates up to Abundance for Wolves

et al. 2021

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Management of wildlife populations often requires reliable estimates of population size or distribution. Estimating abundance can be logistically difficult, and occupancy models have been used as a less expensive proxy for abundance estimation. Another alternative is to use independent estimates of home‐range size and mean group size to directly scale occupancy estimates up to abundance. We used simulations to explore when scaling occupancy up to abundance is reliable, and as an example we applied an occupancy approach to estimate abundance of wolves (Canis lupus) from roadside snow‐tracking surveys in northern Wisconsin, USA, in 2016 and 2018. Estimates of wolf abundance were plausible and compared favorably with independent estimates produced by territory mapping, and snow‐tracking data requirements were lower than for territory mapping. Simulation results suggested that reasonable abundance estimates could be obtained under some conditions but also that severe positive bias could result under other conditions, especially when populations were small and dispersed, home range size was small, and areal sampling units were large. Positive bias in abundance estimates occurs because of closure assumption violations when tracks from a single wolf or pack are detected in >1 sample unit, and the sum of the sample unit areas where tracks were detected exceed the sum of the home range areas. Bias was minimized when sampling units were small relative to home range size or when sampling units were route segments that approximate point sample units, and when home ranges were highly aggregated. We conclude that, although caution is warranted when scaling occupancy estimates up to abundance, scaled occupancy models can provide feasible and reliable estimates of abundance, assuming home range size and mean group size are accurately known or estimated, sampling units are appropriately chosen, and covariates that aggregate home ranges can be used to accurately predict occupancy probability. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.

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“…Surveys occurred 1–3 days after a snowfall event, and the probability

δ_{j}

ψ_{i}

and

θ_{ij}

as in Crosby and Porter (2018);

ψ_{i}

was the probability that a transect passed through ≥1 home range, and

θ_{ij} = P {(u}_{ij} = 1)

was the probability that route segment j on route i intersected ≥1 home range. In our simulated data,

u_{ij} = 1

when a segment j in sample unit i intersected a home range.…”

Section: Methodsmentioning

confidence: 99%

δ

Section: Methodsmentioning

confidence: 99%

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Scaling Occupancy Estimates up to Abundance for Wolves

et al. 2021

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“…1). We relied on subsampling within a spatial unit rather than temporally repeated visits to obtain repeated surveys of the primary sample location (i.e., space-for-time substitution [Kendall & White 2009;Pavlacky et al 2012;Crosby & Porter 2018]) (Appendix S1). We considered a single U.S. Census tract (defined as a relatively small subdivision of a county containing 1200-8000 people [Fig.…”

Section: Nested Subsamples and Occupancy Estimationmentioning

confidence: 99%

Improving inferences about private land conservation by accounting for incomplete reporting

Williamson

Dickson

Hooten

et al. 2021

Conservation Biology

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Private lands provide key habitat for imperiled species and are core components of function protectected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

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“…Multispecies occupancy models provide robust parameter estimates for species infrequently encountered during biodiversity surveys while correcting for sampling bias ( 20 ). Moreover, the advent of multiscale occupancy models accounts for the complexity of habitat selection ( 21 ), but to date, applications have been limited to single-species approaches ( 22 , 23 ). Thus, the formal integration of multispecies methods within a multiscale framework provides a powerful statistical tool to capture hierarchical habitat selection for vulnerable and rare species.…”

mentioning

confidence: 99%

Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations

Deere

Guillera‐Arroita

Swinfield

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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Tropical forest ecosystems are facing unprecedented levels of degradation, severely compromising habitat suitability for wildlife. Despite the fundamental role biodiversity plays in forest regeneration, identifying and prioritizing degraded forests for restoration or conservation, based on their wildlife value, remains a significant challenge. Efforts to characterize habitat selection are also weakened by simple classifications of human-modified tropical forests as intact vs. degraded, which ignore the influence that three-dimensional (3D) forest structure may have on species distributions. Here, we develop a framework to identify conservation and restoration opportunities across logged forests in Borneo. We couple high-resolution airborne light detection and ranging (LiDAR) and camera trap data to characterize the response of a tropical mammal community to changes in 3D forest structure across a degradation gradient. Mammals were most responsive to covariates that accounted explicitly for the vertical and horizontal characteristics of the forest and actively selected structurally complex environments comprising tall canopies, increased plant area index throughout the vertical column, and the availability of a greater diversity of niches. We show that mammals are sensitive to structural simplification through disturbance, emphasizing the importance of maintaining and enhancing structurally intact forests. By calculating occurrence thresholds of species in response to forest structural change, we identify areas of degraded forest that would provide maximum benefit for multiple high-conservation value species if restored. The study demonstrates the advantages of using LiDAR to map forest structure, rather than relying on overly simplistic classifications of human-modified tropical forests, for prioritizing regions for restoration.

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A spatially explicit, multi‐scale occupancy model for large‐scale population monitoring

Cited by 5 publications

References 48 publications

Scaling Occupancy Estimates up to Abundance for Wolves

Scaling Occupancy Estimates up to Abundance for Wolves

Improving inferences about private land conservation by accounting for incomplete reporting

Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations

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