Species Occupancy Modeling for Detection Data Collected Along a Transect

Guillera‐Arroita, Gurutzeta; Morgan, Byron J. T.; Ridout, Martin S.; Linkie, Matthew

doi:10.1007/s13253-010-0053-3

Cited by 69 publications

(108 citation statements)

References 15 publications

(24 reference statements)

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“…; Guillera‐Arroita et al . ). One approach describes the detection process as a continuous point process, where detections occur randomly along a continuous axis (Poisson process) or potential clustering in detections are accounted for via a Markov modulating Poisson process (Guillera‐Arroita et al .…”

Section: Motivation For New Model Developmentmentioning

confidence: 97%

Advances and applications of occupancy models

2013

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Summary1. The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification. 2. We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator. 3. Unlike many mark-recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute 'detection' of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions. 4. We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian-pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development.

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Section: Motivation For New Model Developmentmentioning

confidence: 97%

Advances and applications of occupancy models

2013

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“…() developed a model that explicitly accounts for Markovian dependence in local occupancy at the level of spatial replicates for scenarios where sampling is restricted to linear features in a landscape (e.g. successive segments along a forest trail), and Guillera‐Arroita, Morgan, Ridout, and Linkie (), Guillera‐Arroita, Ridout, Morgan, and Linkie () developed a modelling approach that treats detection as a continuous point process, eliminating the need to divide sampled trails into discrete segments.…”

Section: Introductionmentioning

confidence: 99%

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Srivathsa

Puri

Kumar

et al. 2017

Journal of Applied Ecology

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Occupancy models that account for detection probability are important analytical tools in conservation monitoring. Traditionally, occupancy models relied on detection/non‐detection data generated from temporal replicates for estimating detectability. Due to logistical challenges and financial costs involved, many large‐scale field studies instead use spatial replication as a surrogate. The efficacy of the two approaches and their statistical validity has generally sought support from simulation‐based inferences rather than empirical data. Using the sloth bear Melursus ursinus as an example, we compared estimates of occupancy and detection probabilities obtained from temporal and spatial sampling designs. We carried out temporally replicated camera trap surveys and spatially replicated sign surveys across a 754‐km2 area around Bhadra Tiger Reserve in the Western Ghats of India. We sampled along forest/coffee plantation roads in 58 grid cells of 13 km2 each, treating these cells as independent sites. We used the standard single‐season model for the camera trap survey data, and the single‐season correlated detections model (with Markovian dependence) for the sign survey data, and incorporated ecological covariates that likely influenced occupancy and detection probabilities. Occupancy estimates from the two surveys and corresponding modelling approaches were similar [trueψ^cfalse(trueSE^false) = 0.58 (0.03) for camera trap surveys; trueψ^sfalse(trueSE^false) = 0.56 (0.03) for sign surveys]. In both cases, the influence of covariates corroborated our a priori predictions. Site‐level estimates of occupancy from the two methods were highly correlated (r = .78). We generated a combined estimate of sloth bear occupancy in the region as an inverse‐variance weighted average of the two estimates [normalψtrue^false(trueSE^false) = 0.57 (0.02)]. Synthesis and applications. Studies that aim to evaluate occupancy models should account for spatial variation in occupancy/detection probabilities, particularly when making inferences on species–habitat relationships. We show that spatial replication can serve as a good surrogate for temporal replication in occupancy studies, which may be useful for distribution assessments of species when field resources are limited or logistical challenges preclude traditional survey approaches that yield temporally replicated data. Our results therefore provide a basis for efficient targeting of funds and field resources, particularly for practitioners involved in monitoring species at large landscape scales.

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“…Collecting data in ways that allow the detection process to be modelled is often considered important to minimize the impact of false absences, especially in the case of animals (MacKenzie et al 2002;Lahoz-Monfort et al 2013;Stauffer et al 2002). This is often done by repeatedly surveying a given site, but other methods are possible such as recording times to detection (Guillera-Arroita et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

et al. 2014

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Sustainable land management requires scientists to provide reliable data on diversity distribution patterns. Resource restrictions limit the affordable sampling effort, both with respect to number of survey sites and amount of effort per site. We compared different levels of survey effort in a case study in Central Romania, varying the number of repeats per site and number of survey sites. Target taxa were plants, birds and butterflies. For plants, we surveyed three 10 m 2 plots and ten plots of 1 m 2 at each site. For birds, we used point counts and for butterflies Pollard walks, in both cases with four repeats. We fitted hierarchical community models to estimate true species richness per site. Estimates of true species richness per site strongly correlated with observed species richness. However, hierarchical community models yielded unrealistically high estimates of true species richness per site, hence we used observed richness for further analyses. For each species group, we compared diversity indices from subsets of the dataset with the full dataset. Findings obtained with a reduced survey effort reflected well those obtained with full effort. Moreover, we conducted a power analysis to assess how the number of survey sites affected the minimum detectable effect of landscape heterogeneity on species richness, and found there was an exponential decrease in the minimum detectable effect with increasing number of sites. In combination, our findings suggest that assessing broad 123Biodivers Conserv (2015) 24:33-46 DOI 10.1007 diversity patterns in abundant and readily detectable organisms may be possible with relatively low survey effort per site. Our study demonstrates the utility of conducting pilot studies prior to designing large-scale studies on diversity distribution patterns.

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Species Occupancy Modeling for Detection Data Collected Along a Transect

Cited by 69 publications

References 15 publications

Advances and applications of occupancy models

Advances and applications of occupancy models

Substituting space for time: Empirical evaluation of spatial replication as a surrogate for temporal replication in occupancy modelling

Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

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