Simulations inform design of regional occupancy‐based monitoring for a sparsely distributed, territorial species

Latif, Quresh S.; Ellis, Martha M.; Saab, Victoria A.; Mellen‐McLean, Kim

doi:10.1002/ece3.3725

Cited by 17 publications

(17 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, Latif et al. ), while accounting for observation errors such as imperfect detection (Guillera‐Arroita and Lahoz‐Monfort ). In some cases, the cost of visiting and sampling sites has been integrated with power analysis to explore trade‐offs between the number of sites, and the frequency and duration of monitoring given fixed budgets and objectives (Field et al.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of biological monitoring, power analysis has primarily focused on assessing the probability of detecting changes in abundance (Rhodes et al 2006) or occupancy (Strayer 1999, Steenweg et al 2016, Latif et al 2018, while accounting for observation errors such as imperfect detection (Guillera-Arroita and Lahoz-Monfort 2012). In some cases, the cost of visiting and sampling sites has been integrated with power analysis to explore trade-offs between the number of sites, and the frequency and duration of monitoring given fixed budgets and objectives (Field et al 2005).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatially explicit power analysis for detecting occupancy trends for multiple species

Southwell

Einoder²,

Lahoz‐Monfort

et al. 2019

Ecological Applications

View full text Add to dashboard Cite

Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species‐level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long‐term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera‐trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision‐makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatially explicit power analysis for detecting occupancy trends for multiple species

Southwell

Einoder²,

Lahoz‐Monfort

et al. 2019

Ecological Applications

View full text Add to dashboard Cite

show abstract

“…For example, occupancy models fit with constant

\overset{p}{true}

overestimated occupancy trends because detection (which varied annually with abundance) and occupancy were confounded (Latif et al. ). Occurrence or prevalence of systematic changes in p is difficult to generalize because this likely varies by study system and research question.…”

Section: Discussionmentioning

confidence: 99%

“…Our results can be extended to other systems where N-mixture models are applied, including single-season studies with closed populations that vary across space rather than time (K ery 2008, Yamaura 2013, Veech et al 2016, and more broadly to other hierarchical models that account for detectability. For example, occupancy models fit with constantp overestimated occupancy trends because detection (which varied annually with abundance) and occupancy were confounded (Latif et al 2018). Occurrence or prevalence of systematic changes in p is difficult to generalize because this likely varies by study system and research question.…”

Section: Discussionmentioning

confidence: 99%

The importance of simulation assumptions when evaluating detectability in population models

et al. 2019

View full text Add to dashboard Cite

Population monitoring is important for investigating a variety of ecological questions, and N‐mixture models are increasingly used to model population size (N) and trends (λ) while estimating detectability (p) from repeated counts within primary periods (when populations are closed to changes). Extending these models to dynamic processes with serial dependence across primary periods may relax the closure assumption, but simulations to evaluate models and inform effort (e.g., number of repeated counts) typically assume p is constant or random across sites and years. Thus, it is unknown how these models perform under scenarios where trends in p confound inferences on N and λ, and conclusions regarding effort may be overoptimistic. Here, we used global positioning system data from greater sage‐grouse (Centrocercus urophasianus) to inform simulations of the detection process for lek counts of this species, and we created scenarios with and without linear annual trends in p. We then compared estimates of N and λ from hierarchical population models either fit with single maximum counts or with detectability estimated from repeated counts (dynamic N‐mixture models). We also explored using auxiliary data to correct counts for variation in detectability. Uncorrected count models consistently underestimated N by >50%, whereas N‐mixture models without auxiliary data underestimated N to a lesser degree due to unmodeled heterogeneity in p such as age. Nevertheless, estimates of λ from both types of models were unbiased and similar for scenarios without trends in p. When p declined systematically across years, uncorrected count models underestimated λ, whereas N‐mixture models estimated λ with little bias when all sites were counted repeatedly. Auxiliary data also reduced bias in parameter estimates. Evaluating population models using scenarios with systematic variation in p may better reveal potential biases and inform effort than simulations that assume p is constant or random. Dynamic N‐mixture models can distinguish between trends in p and N, but also require repeated counts within primary periods for accurate estimates. Auxiliary data may be useful when researchers lack repeated counts, wish to monitor more sites less intensively, or require unbiased estimates of N.

show abstract

“…We modelled the probability of detection, p jkt , as a function of survey interval duration (2 min = 0, 3 min = 1), the Julian day of the year, and the survey type (passive = 0, broadcast = 1). Given that black-backed woodpeckers exist at low densities even within ideal habitat and have primarily nonoverlapping territories (Tingley et al, 2014), we did not seek to control for the potential effect of abundance on detection probability (Royle & Nichols, 2003) which can potentially bias estimates of occupancy (Latif, Ellis, Saab, & Mellen Mclean, 2018).…”

Section: Modelling Approachmentioning

confidence: 99%

Cross‐scale occupancy dynamics of a postfire specialist in response to variation across a fire regime

Tingley

Stillman

Wilkerson

et al. 2018

Journal of Animal Ecology

View full text Add to dashboard Cite

Fire creates challenges and opportunities for wildlife through rapid destruction, modification and creation of habitat. Fire has spatially variable effects on landscapes; however, for species that benefit from the ephemeral resource patches created by fire, it is critical to understand characteristics of fires that promote postfire colonization and persistence and the spatial scales on which they operate. Using a model postfire specialist, the black-backed woodpecker (Picoides arcticus), we examined how colonization and persistence varied across two spatial scales as a function of four characteristics of fire regimes-fire severity, fire size, fire ignition date and number of years since fire. We modelled black-backed woodpecker colonization and persistence using data from 108 recently burned forests in the Sierra Nevada and southern Cascades ecoregions of California, USA, that we monitored for up to 10 years following fire. We employed a novel, spatially hierarchical, dynamic occupancy framework which differentiates colonization and persistence at two spatial scales: across fires and within fires. We found strong effects of fire characteristics on dynamic rates, with colonization and persistence declining across both spatial scales with increasing years since fire. Additionally, at sites within fires, colonization decreased with fire size and increased with fire severity and for fires with later ignition dates. Our results support the notion that different aspects of a species' environment are important for population processes at different spatial scales. As habitat quality is ephemeral for any given postfire area, our results illustrate the importance of time since fire in structuring occupancy at the fire level, with other characteristics of fires playing larger roles in determining abundance within individual fires. Our results contribute to the broader understanding of how variation in fire characteristics influences the colonization and persistence of species using ephemeral habitats, which is necessary for conserving and promoting postfire biodiversity in the context of rapidly shifting fire regimes.

show abstract

Simulations inform design of regional occupancy‐based monitoring for a sparsely distributed, territorial species

Cited by 17 publications

References 51 publications

Spatially explicit power analysis for detecting occupancy trends for multiple species

Spatially explicit power analysis for detecting occupancy trends for multiple species

The importance of simulation assumptions when evaluating detectability in population models

Cross‐scale occupancy dynamics of a postfire specialist in response to variation across a fire regime

Contact Info

Product

Resources

About