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

While 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 1) their imperfect detectability, 2) their dynamic ranges over time and 3) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves Canis lupus that have been recolonizing France since the early 1990s. 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 1994 to 2016, 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 more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500 m and positively influenced by the number of observed occupied sites at short and long‐distances, forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. 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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“…Data available from the Dryad Digital Repository: < http:// dx.doi.org/10.5061/dryad.g9s1d > (Louvrier et al 2017).…”

Section: Data Depositionmentioning

confidence: 99%

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

et al. 2017

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“…To add more spatial constraints without changing the model structure, a new individual characteristic could be defined to represent distances between individuals regarding their pack affiliation. Individuals from the same pack being closer to each other than from other individuals and therefore having the possibility to define small-distance dispersers vs long-distance dispersers (Louvrier et al, 2018), different from the immigration/emigration process of our model. With a bit of more work, the model could be turned into a spatially explicit IBM by including an explicit dispersal sub-model like in Marucco and McIntire (2010) in place of the current dispersal sub-model thanks to the modularity of our IBM structure.…”

Section: Discussionmentioning

confidence: 99%

Exploring the impact of lesser-known social dynamics on wolf populations through an individual-based approach

Bauduin¹,

Grente²,

Santostasi

et al. 2020

Preprint

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Highlights-We developed an individual-based model (IBM) simulating wolf population dynamics.-The IBM incorporates up-to-date knowledge of pack structure and dynamics.-The IBM is flexible and modular and can be adapted to any wolf populations.-The IBM is written in R language to make its use by ecologists easy. AbstractThe presence of wolf populations in human-dominated landscapes is challenging worldwide because of conflicts with human activities. Modeling is an important tool to predict wolf dynamics and expansion and help in decision making concerning management and conservation.Here we present an individual-based model (IBM) to project wolf population dynamics. IBMs are bottom-up models that simulate the fate of individuals interacting with each other, with population-level properties emerging from the individual-level simulations. IBMs are particularly adapted to represent social species such as the wolf that exhibits complex individual interactions.Our IBM predicts wolf demography including fine-scale individual behavior and pack dynamics processes based on up-to-date scientific literature. The model extends previous attempts to represent wolf population dynamics, as we included important biological processes that were not previously considered such as pack dissolution, asymmetric male and female breeder replacement by dispersers or subordinates, inbreeding avoidance, establishment of dispersing individuals by budding, adoption of young dispersing wolves, long distance dispersal, and density-dependent mortality. We demonstrate two important aspects of our IBM (i.e., modularity and flexibility) by running different series of the processes representing the wolf life cycle. The simulations point out the importance of data records on these biological components when managers are willing to promote wolf population conservation and management strategies. This exercise also shows that the model can flexibly include or exclude different processes therefore being applicable to wolf populations experiencing different ecological and demographic conditions. The model is coded in R to facilitate its understanding, appropriation and adaptation by ecologists. Overall, our model allows testing different scenarios of wolf dynamics, disturbances and alternative management strategies to project wolf populations, and therefore inform decision making to improve wolf management and conservation.

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“…However, we could only quantify the sampling effort between years, and had no information at the month level. Second, it is also likely that the individual-level detectability varies between months partly due to the varying sampling effort between months, but also to environmental conditions, such a snow cover represented by the month of survey (Louvrier et al 2018). Third, the local abundance at a site is also likely to change between surveys.…”

Section: 3model Assumptionsmentioning

confidence: 99%

“…In Louvrier et al (2018), a dynamic site-occupancy model was fitted to the same dataset, at a national level and between 1994 and 2016. We found in this previous study that when forest cover was high, the probability for an unoccupied site to be colonized the year after increased as well.…”

Section: Comparison With Dynamic Site-occupancy Modelsmentioning

confidence: 99%

“…In this context, opportunistic data produced by semi-structured citizen science are increasingly used as an efficient source of information to assess the dynamics of such species (Schmeller et al 2009;Louvrier et al 2018;Kelling et al 2019). The monitoring system often relies on the only available opportunistic data, leading to a set of presence locations, without any information about absences (Koshkina et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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A mechanistic–statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France

Louvrier

Papaïx

Duchamp³

et al. 2020

Spatial Statistics

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Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution.Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate spatio-temporal processes in ecological models. As a case study, we considered the wolf (Canis lupus) that has been recolonizing Eastern France naturally through dispersal from the Apennines since the early 90's. Using partial differential equations for modelling species diffusion and growth in a fragmented landscape, we develop a mechanisticstatistical spatio-temporal model accounting for ecological diffusion, logistic growth and imperfect species detection. We conduct a simulation study and show the ability of our model to i) estimate ecological parameters in various situations with contrasted species detection probability and number of surveyed sites and ii) forecast the distribution into the future. We found that the growth rate of the wolf population in France was explained by the proportion of forest cover, that diffusion was influenced by human density and that species detectability increased with increasing survey effort. Using the parameters estimated from the 2007-2015 period, we then forecasted wolf distribution in 2016 and found good agreement with the actual detections made that year. Our approach may be useful for managing species that interact with human activities to anticipate potential conflicts.

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

Cited by 20 publications

References 77 publications

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

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

Exploring the impact of lesser-known social dynamics on wolf populations through an individual-based approach

A mechanistic–statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France

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