Comparing profile methods and site-occupancy modelling for the study of occurrence of an elusive species

Rolland, Jonathan; Basille, Mathieu; Marboutin, Éric; Gaillard, Jean‐Michel

doi:10.1007/s10344-011-0549-3

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…We then fit a site‐occupancy model (MacKenzie et al . ) with site‐dependent ψ s and site‐specific detection probability p Os as suggested by a previous analysis of the data (Rolland, Basille & Marboutin ). The site‐dependent occupancy probability is defined by the mean occupancy μ ψ and a site random effect β s normally distributed with mean 0 and variance

σ_{β}^{2}

.…”

Section: Methodsmentioning

confidence: 68%

“…To overcome these issues, monitoring a carnivore population may be performed at various levels of resolution (Gese ). Biologists often resort to large‐scale sign survey to provide information on species distribution or relative abundance at a lesser cost (mustelids: Ruiz‐Olmo, Saavedra & Jiménez (); felids: Rolland, Basille & Marboutin (); canids: Llaneza et al . ()).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Blanc

Marboutin

Gatti

et al. 2014

Journal of Applied Ecology

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Summary1. Abundance is a key quantity for conservation and management strategies but remains challenging to assess in the field. Capture-recapture (CR) methods are often used to estimate abundance while correcting for imperfect detection, but these methods are costly. Occupancy, sometimes considered as a surrogate for abundance, is estimated through the collection of presence/absence data and is less costly while allowing gathering of information at a large spatial scale. 2. Building on the recent pieces of work on the combination of different data sources, we showed how abundance data can be complemented by presence/absence data and can be analysed conjointly to improve abundance estimates. Our approach relies on a hierarchical model that makes explicit the link between the abundance and occupancy state variables while formally accounting for imperfect detection. 3. We used a population of Eurasian lynx in France monitored via camera traps and a collection of presence signs as an illustration of our approach. 4. Synthesis and applications. We combined capture-recapture and occupancy data and demonstrated that we can efficiently improve abundance estimates. Our method can be used by managers when estimates of trends in abundance lack power due to sparse data collected during an intensive survey, by simply integrating data collected during non-systematic survey. Furthermore, combining these two sampling procedures makes full use of all available data and allows the development of conservation and management strategies based on precise abundance estimates. Overall, the combination of different data sources in an integrated statistical framework has great potential, especially for elusive species.

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σ_{β}^{2}

.…”

Section: Methodsmentioning

confidence: 68%

Section: Methodsmentioning

confidence: 99%

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Blanc

Marboutin

Gatti

et al. 2014

Journal of Applied Ecology

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show abstract

“…This is especially true for rare and elusive species, which are frequently of high conservation concern (Rolland et al. ). Large carnivores are no exception to this (Kindberg et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, reliable estimates are difficult to obtain. This is especially true for rare and elusive species, which are frequently of high conservation concern (Rolland et al 2011). Large carnivores are no exception to this (Kindberg et al 2009;Creel and Rosenblatt 2013) as they are generally solitary and cryptic, in addition to occurring at low densities and across large home ranges.…”

Section: Introductionmentioning

confidence: 99%

Estimating population size using single‐nucleotide polymorphism‐based pedigree data

Spitzer

Norman

Schneider

et al. 2016

Ecology and Evolution

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Reliable population estimates are an important aspect of sustainable wildlife management and conservation but can be difficult to obtain for rare and elusive species. Here, we test a new census method based on pedigree reconstruction recently developed by Creel and Rosenblatt (2013). Using a panel of 96 single‐nucleotide polymorphisms (SNPs), we genotyped fecal samples from two Swedish brown bear populations for pedigree reconstruction. Based on 433 genotypes from central Sweden (CS) and 265 from northern Sweden (NS), the population estimates (N = 630 for CS, N = 408 for NS) fell within the 95% CI of the official estimates. The precision and accuracy improved with increasing sampling intensity. Like genetic capture–mark–recapture methods, this method can be applied to data from a single sampling session. Pedigree reconstruction combined with noninvasive genetic sampling may thus augment population estimates, particularly for rare and elusive species for which sampling may be challenging.

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“…Based on signs of presence collected between 2002 and 2006 by a network of trained volunteers, Rolland et al . () modelled the occurrence of the Eurasian lynx ( Lynx lynx ) in the French Jura mountains. Briefly, a grid with cells of 9 × 9 km was used, resulting in 197 sites, and the study period was split into five 1‐year periods.…”

Section: A Real‐world Example Illustrating Heterogeneity In Species Dmentioning

confidence: 99%

Fitting occupancy models with E‐SURGE: hidden Markov modelling of presence–absence data

et al. 2014

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Summary1. Occupancy -the proportion of area occupied by a species -is a key notion for addressing important questions in ecology, biogeography and conservation biology. Occupancy models allow estimating and inferring about species occurrence while accounting for false absences (or imperfect species detection). 2. Occupancy models can be formulated as hidden Markov models (HMM) in which the state process captures the Markovian dynamic of the actual but latent states, while the observation process consists of observations that are made from these underlying states. 3. We show how occupancy models can be implemented in program E-SURGE, which was initially developed to analyse capture-recapture data in the HMM framework. Replacing individuals by sites provides the user with access to several features of E-SURGE that are not available altogether or just not available in standard occupancy software: i) flexible model specification through a user-friendly syntax without having to write custom code, ii) decomposition of the observation and state processes in several steps to provide flexible parameterisation, iii) up-to-date diagnostics of model identifiability, and iv) advanced numerical algorithms to produce fast and reliable results (including site random effects). 4. To illustrate E-SURGE features, we provide implementation and analysis details for several occupancy models. We also provide simulated and real-world examples as well as further specifications and information in a companion wiki platform http://occupancyinesurge.wikidot.com/.

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Comparing profile methods and site-occupancy modelling for the study of occurrence of an elusive species

Cited by 4 publications

References 14 publications

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Improving abundance estimation by combining capture–recapture and occupancy data: example with a large carnivore

Estimating population size using single‐nucleotide polymorphism‐based pedigree data

Fitting occupancy models with E‐SURGE: hidden Markov modelling of presence–absence data

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