Occupancy dynamics of wild rabbits (Oryctolagus cuniculus) in the coastal dunes of the Netherlands with imperfect detection

Context Wild rabbits are considered a key species in the coastal dunes of the Netherlands, but populations have collapsed as a result of viral diseases. Aim We studied to what extent population collapse led to local extinction and whether recolonisation of empty patches in the dunes happened. Methods We investigated occupancy dynamics using data of 245 transects where rabbits were surveyed in 1984–2009. Dynamic site-occupancy models were used to analyse the data. These models adjust for imperfect detection to avoid bias in occupancy-trend estimation. Key results The decline of the rabbit population has resulted in many local extinctions, especially in woodland and in the northern part of the coastal dunes. Most transects along grassland and mixed vegetation have recently been reoccupied. The recovery of woodland occupancy is slow, probably not because of limited dispersal capacity of rabbits, but because the quality of woodland habitats is poor. Detection probability of rabbits varied considerably over the years and among habitat types, indicating the necessity of taking detection into account. Rabbits were slightly better detected when it was cloudy, windy and rainy and when lunar phase approached new moon. Conclusion Extinction and recolonisation of habitat patches varied considerably among habitat types. Implications The current slow recolonisation hampers the recovery of rabbit populations in woodland habitats in the Dutch coastal dunes. Furthermore, monitoring rabbit occupancy should take imperfect detection into account to avoid biased results.

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Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

10.1101/099424 ◽

2017 ◽

Author(s):

Julie Louvrier ◽

Christophe Duchamp ◽

Eric Marboutin ◽

Sarah Cubaynes ◽

Rémi Choquet ◽

...

Keyword(s):

Forest Cover ◽

Site Occupancy ◽

Sampling Effort ◽

Large Carnivores ◽

Occupancy Models ◽

Distribution Models ◽

Imperfect Detection ◽

Professional Activities ◽

A Cell ◽

Over Time

AbstractWhile large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to i) their imperfect detectability, ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Not accounting for these issues can lead to flawed inference about the distribution.Here, we focused on the wolf (Canis lupus) that has been recolonizing France since the early 90’s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1993 to 2014, while accounting for species imperfect detection and time- and space-varying sampling effort using dynamic site-occupancy models.Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by 50% on average. Colonization increased with increasing number of occupied sites at short and long-distances, as well as with increasing forest cover, farmland cover and mean altitude. Colonization decreased when high-altitude increased. The growth rate, defined as the number of sites newly occupied in a given year divided by the number of occupied sites in the previous year, decreased over time, from over 100% in 1994 to 5% in 2014. This suggests that wolves are expanding in France but at a rate that is slowing down. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decision-makers to target sites where large carnivores are likely to occur and mitigate conflicts.

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Occupancy patterns of prey species in a biological corridor and inferences for tiger population connectivity between national parks in Bhutan

Oryx ◽

10.1017/s0030605320000976 ◽

2021 ◽

pp. 1-8

Author(s):

Letro Letro ◽

Klaus Fischer ◽

Dorji Duba ◽

Tandin Tandin

Keyword(s):

Wild Boar ◽

National Parks ◽

Prey Species ◽

Site Occupancy ◽

Population Connectivity ◽

Panthera Tigris ◽

Occupancy Models ◽

Imperfect Detection ◽

Occupancy Probability ◽

Biological Corridor

Abstract Site occupancy models, accounting for imperfect detection and the influence of anthropogenic and ecological covariates, can indicate the status of species populations. They may thus be useful for exploring the suitability of landscapes such as biological corridors, to ensure population dispersal and connectivity. Using occupancy probability models of its principal prey species, we make inferences on landscape connectivity for the movement of the tiger Panthera tigris between protected areas in Bhutan. We used camera-trap data to assess the probability of site occupancy (Ψ) of the sambar Rusa unicolor, wild boar Sus scrofa and barking deer Muntiacus muntjak in biological corridor no. 8, which connects two national parks in central Bhutan. At least one prey species was recorded at 17 out of 26 trapping locations. The probability of site occupancy was highest for the barking deer (Ψ = 0.52 ± SE 0.09) followed by sambar (Ψ = 0.49 ± SE 0.03) and wild boar (Ψ = 0.45 ± SE 0.07). All three species had higher occupancy probability at lower altitudes. Sambar occupancy was greater farther from settlements and on steeper and/or south-facing slopes. Barking deer also had higher occupancy on south-facing slopes, and wild boar occurred mainly close to rivers. Our findings suggest that this biological corridor could facilitate dispersal of tigers. Protecting prey species, and minimizing anthropogenic disturbance and habitat fragmentation, are vital for tiger dispersal and thus functional connectivity amongst populations in this area.

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Faculty Opinions recommendation of A note on simplifying likelihoods for site occupancy models.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1086914.539849 ◽

2007 ◽

Author(s):

Philip Dixon

Keyword(s):

Site Occupancy ◽

Occupancy Models

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Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys

Royal Society Open Science ◽

10.1098/rsos.160368 ◽

2016 ◽

Vol 3 (10) ◽

pp. 160368 ◽

Cited By ~ 4

Author(s):

Campbell Murn ◽

Graham J. Holloway

Keyword(s):

Detection Probability ◽

Spatial Scales ◽

Survey Methods ◽

A Priori ◽

Low Density ◽

Occupancy Models ◽

Imperfect Detection ◽

Factors Affecting ◽

Sources Of Variation ◽

Time Of Year

Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.

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A sparse observation model to quantify species interactions in time and space

10.1101/815027 ◽

2019 ◽

Author(s):

Sadoune Ait Kaci Azzou ◽

Liam Singer ◽

Thierry Aebischer ◽

Madleina Caduff ◽

Beat Wolf ◽

...

Keyword(s):

Species Interactions ◽

Activity Patterns ◽

Niche Partitioning ◽

Camera Trap ◽

Camera Traps ◽

Observation Model ◽

Occupancy Models ◽

Time And Space ◽

Imperfect Detection ◽

Mobile Species

SummaryCamera traps and acoustic recording devices are essential tools to quantify the distribution, abundance and behavior of mobile species. Varying detection probabilities among device locations must be accounted for when analyzing such data, which is generally done using occupancy models. We introduce a Bayesian Time-dependent Observation Model for Camera Trap data (Tomcat), suited to estimate relative event densities in space and time. Tomcat allows to learn about the environmental requirements and daily activity patterns of species while accounting for imperfect detection. It further implements a sparse model that deals well will a large number of potentially highly correlated environmental variables. By integrating both spatial and temporal information, we extend the notation of overlap coefficient between species to time and space to study niche partitioning. We illustrate the power of Tomcat through an application to camera trap data of eight sympatrically occurring duiker Cephalophinae species in the savanna - rainforest ecotone in the Central African Republic and show that most species pairs show little overlap. Exceptions are those for which one species is very rare, likely as a result of direct competition.

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Derivation of Critical Loads of Nitrogen for Habitat Types and Their Exceedances in the Netherlands

Environmental Pollution - Critical Loads and Dynamic Risk Assessments ◽

10.1007/978-94-017-9508-1_22 ◽

2015 ◽

pp. 547-572

Author(s):

Han F. van Dobben ◽

Arjen van Hinsberg ◽

Dick Bal ◽

Janet P. Mol-Dijkstra ◽

Henricus J.J. Wieggers ◽

...

Keyword(s):

The Netherlands ◽

Critical Loads ◽

Habitat Types

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Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2019-0125 ◽

2020 ◽

Vol 77 (3) ◽

pp. 602-610

Author(s):

Shannon White ◽

Evan Faulk ◽

Caleb Tzilkowski ◽

Andrew Weber ◽

Matthew Marshall ◽

...

Keyword(s):

Species Richness ◽

Fish Community ◽

Fish Assemblages ◽

Similar Species ◽

Occupancy Models ◽

Imperfect Detection ◽

Modeling Framework ◽

Bayesian Hierarchical ◽

Recreation Area ◽

Species Occupancy

Understanding how stream fishes respond to changes in habitat availability is complicated by low occurrence rates of many species, which in turn reduces the ability to quantify species–habitat relationships and account for imperfect detection in estimates of species richness. Multispecies occupancy models have been used sparingly in the analysis of fisheries data, but address the aforementioned deficiencies by allowing information to be shared among ecologically similar species, thereby enabling species–habitat relationships to be estimated for entire fish communities, including rare species. Here, we highlight the utility of hierarchical multispecies occupancy models for the analysis of fish community data and demonstrate the modeling framework on a stream fish community dataset collected in the Delaware Water Gap National Recreation Area, USA. In particular, we demonstrate the ability of the modeling framework to make inferences at the species-, guild-, and community-levels, thereby making it a powerful tool for understanding and predicting how environmental variables influence species occupancy probabilities and structure fish assemblages.

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