Biology subjects

Gimenez, O.

Publications and source records attributed to Gimenez, O..

At least 19 recordsLinked to original sources

bioRxiv2026

Environmental DNA enables rapid detection of invasive coypu and complements camera trapping

Effective management of invasive species requires surveillance methods that reliably detect populations while minimizing field effort. Environmental DNA (eDNA) offers a potentially rapid alternative to conventional monitoring, but direct comparisons with other non-invasive methods remain limited for invasive mammals. Here, we compared eDNA water sampling and camera trapping for detecting established populations of coypu (Myocastor coypus), an invasive semi-aquatic rodent. Using replicated data collected across Mediterranean river systems in southern France, we estimated detection probabilities conditional on coypu presence using Bayesian generalized linear mixed models and quantified the sampling effort required to achieve high cumulative detection probability. Detection probability was 93.0% (95% credible interval: 72.1-98.9%) for a single eDNA water sample, compared with 52.8% (22.6-91.1%) for 30 camera-trap days, with a 96.9% posterior probability that eDNA detection was higher. Two eDNA samples were sufficient to achieve a median cumulative detection probability above 95%, compared with four 30-day camera-trapping periods. Detection also varied at different spatial scales, with camera-trap detectability being heterogeneous among local camera locations and eDNA detectability varying more among catchments. These results show that eDNA provides a rapid and reliable approach for confirming the presence of established coypu populations, while camera traps provide complementary information on activity, behaviour and habitat use. Combining rapid eDNA screening with targeted camera trapping may therefore provide an efficient surveillance strategy for invasive semi-aquatic mammals.

ecology↗

bioRxiv2026

Using stochastic dynamic programming for making decisions about invasive species

1. Invasive species pose significant ecological, economic, and public health challenges. Their management requires repeated decisions under uncertainty about when, where and how intensively to intervene. Although structured decision-making and adaptive management provide conceptual frameworks for addressing these challenges, their practical implementation remains limited because optimization methods such as stochastic dynamic programming (SDP) are often perceived as mathematically complex, challenting to apply and difficult to interpret. 2. Here, we provide a practical guide to using SDP and its extensions for invasive species management. Using coypu (*Myocastor coypus*) management as a running case study, we introduce the framework through a minimal working example before progressively extending it to spatial dynamics, imperfect detection using partially observable Markov decision processes (POMDPs), adaptive management under model uncertainty, and value-of-information analyses that quantify the benefits of learning before acting. 3. All examples are accompanied by reproducible implementations in R, together with recommendations on model formulation, parameterization and interpretation. We also discuss the strengths and limitations of SDP. 4. By lowering the technical barrier to SDP, this guide aims to help ecologists and wildlife managers integrate decision theory into invasive species management, enabling transparent, reproducible and objective-driven management strategies under uncertainty. More broadly, the methods presented here are applicable to a wide range of ecological decision problems beyond invasive species, wherever management requires balancing immediate actions against uncertain future outcomes.

ecology↗

bioRxiv2026

Comparative efficiency of eDNA, camera traps and scat surveys to detect a semi-aquatic mammal across multiple catchments

Semi-aquatic mammals lie at the intersection of several key conservation issues such as wetland deterioration or species invasions, and monitoring their distribution in space and time is essential to inform conservation strategies. However, gathering information about their presence is challenging due to their elusive lifestyle and generally low abundance. The Eurasian otter (Lutra lutra), a near-threatened and strictly protected species in Europe, is currently recolonizing part of its historical range. Its high conservation interest, combined with a dynamic more commonly associated with range-expanding or invasive species, makes it a particularly compelling case study. Otter monitoring has traditionally relied on scat surveys, but recent environmental DNA (eDNA) and camera-trapping initiatives have emerged offering promising complementary tools. Yet, these approaches have rarely been formally compared, either to one another or across regions. Here, we compared the efficiency of spraint surveys, camera traps, and eDNA for detecting otters, and assessed how their performance varied among four catchments in southern France where the species is known to be present. All three methods provided otter detections with varying efficiency. Scat surveys were the most effective method, with an average detection probability of 0.71 and no strong variability between catchments. Although camera-traps had the lowest detection rate, they provided detections at two of the four sites where no spraint was found, highlighting the complementarity of these two approaches. Detection rates varied greatly between individual cameras rather than between catchments, underscoring sensitivity to camera-placement. eDNA showed important variability between catchments, with detection probabilities differing by roughly sixfold across regions. All in all, our results highlight differences in efficiency between methods and across environmental conditions, and show the value of combining approaches for future monitoring programs.

ecology↗

bioRxiv2026

Monte Carlo simulations to propagate the uncertainty of machine-learning classification into ecological models

Deep learning (DL) is increasingly integrated into quantitative ecology, particularly for automating the classification of sensor data in biodiversity monitoring. In addition to substantially reducing data processing effort, DL models often achieve high classification performance. However, despite ongoing improvements, certain species or classification tasks remain challenging, and predictions are rarely error-free. Manual verification is frequently included into data processing pipelines to mitigate misclassifications, but this approach may mask rather than quantify uncertainty. Here, we propose directly incorporating classification uncertainty into ecological inference, rather than filtering it afterwards. Specifically, we treat model predictions as probabilistic outputs rather than fixed class assignments, by using Monte Carlo simulations to propagate uncertainty from the classification process into downstream ecological models. We illustrate this approach using two case studies. The first estimates stochastic population growth rates for a penguin population using detection time series derived from Radio Frequency IDentification (RFID). The second propagates uncertainty in species identification from camera trap images into occupancy estimates. For both, we compare results obtained propagating classification uncertainty with those from conventional single-class attribution at two confidence score thresholds. Our findings show that propagating uncertainty typically leads to higher, more optimistic ecological estimates compared to the single-class confidence approach. Importantly, this method expands the total uncertainty interval by explicitly introducing the confidence score produced by automatic classification as a representation of uncertainty. Quantifying this uncertainty in parameter estimation allows for more informed and reliable ecological interpretations. Monte Carlo simulations offer a flexible and accessible means to integrate classification uncertainty into diverse ecological modelling workflows.

ecology↗

bioRxiv2025

When does temporal resolution matter? Including detection covariates in discrete- versus continuous-time occupancy and N-mixture models

Camera traps and other sensors allow continuous-time biodiversity observation, raising new questions and opportunities for modelling detection in hierarchical models such as occupancy (for species presence) and N-mixture models (for abundance). We focused on a rarely considered aspect: how the temporal treatment of detection covariates affects inference. Through simulations and a five-month case study on an research center, we examined the effects of covariate temporal resolution, discretisation scale in discrete-time (DT) models, and interpolation methods in continuous-time (CT) models. While occupancy and abundance estimates were largely unaffected by these choices, detection estimates were more sensitive to them. DT models with fine temporal discretisation closely matched CT models. Simulations showed that when detection covariates had no effect on detectability, the considered modelling choices had little impact. But when covariates did influence detection, bias and error increased if their temporal variation was not accurately retained. The case study revealed more complex patterns, highlighting the consequences of temporally simplifying both observations and detection covariates. Overall, our results suggest that when detectability is of ecological interest, exploring a range of temporal treatments of detection covariates, from fine-scale to coarser resolutions, can reveal complementary insights into scale-dependent patterns in detection.

ecology↗

bioRxiv2024

How reliably can species co-occurrence be inferred from occupancy models?

Interspecific interactions, such as predation, competition, and mutualism, are fundamental to the structure and function of any biotic community. Understanding these interactions provides valuable insights into community dynamics and informs conservation efforts. One common approach to studying these interactions is through co-occurrence patterns, which remain essential for understanding species relationships. Occupancy models have emerged as powerful tools to investigate these patterns, offering a flexible framework for ecological questions. Among these models, the co-occurrence framework developed by Rota et al. (2016) has seen widespread applications across various species and habitats. In this study, we used simulations to evaluate the performance of the Rota et al. (2016) co-occurrence model under different study designs and species interaction scenarios. Specifically, we examined how the bias and precision of species occupancy estimates are influenced by the presence or absence of another species, varying the number of sampling units surveyed and the number of temporal replicates per unit. Additionally, we compared the co-occurrence models performance with a simpler model that does not account for species interactions to determine when statistically significant co-occurrence patterns emerge. We observed that occupancy parameters become more precise as the intensity of species avoidance increased, and while the number of sampling sites significantly influenced precision, the number of temporal replicates did not. Notably, the co-occurrence model outperformed the non-interaction model only when species avoidance was strong and at least 150 sites were sampled. Based on these results, we recommend a minimum of 150 sampling sites when applying the Rota et al. (2016) model to evaluate co-occurrence between two potentially interacting species. Our study emphasizes the need for further development of methods capable of detecting interaction signals, even at low to moderate interaction levels, and in studies with fewer sampling sites.

evolutionary biology↗

bioRxiv2024

Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France

The lethal control of large carnivores is criticized regarding its efficiency to prevent hotspots of attacks on livestock. Previous studies, mainly focused on North America, provided mixed results. We evaluated the effects of wolf lethal removals on the distribution of attack intensities in the French Alps between 2011 and 2020, using a Before After Control-Impact approach with retrospective data. We built an original framework combining both continuous spatial and temporal scales and a 3D kernel estimation. We compared the attack intensities observed before and after the legal killings of wolves on a period of 90 days and on a range of 10 km, and with control situations where no removal occurred. The analysis was corrected for the presence of livestock. A moderate decrease of attack intensity was the most common outcome after the lethal removal of a single wolf. This reduction was greatly amplified when removing two or three wolves. The scale of analysis also modulated this general pattern, with decreases being generally amplified at a small spatio-temporal range. Contextual factors (e.g., geographical or seasonal variations) could also lead to deviations from this general pattern. Overall, between 2011 and 2020, lethal control of wolves in France generally contributed to reduce livestock attacks, but mainly locally and to a minor extent. Our results highlight the importance of accounting for scale in such assessments and suggest that the evaluation of the effectiveness of lethal removals in reducing livestock predation might be more relevant in a local context. As a next step, we recommend to move forward from patterns to mechanisms by linking the effects of lethal control on wolves to their effects on attacks through analysis of fine-scaled data on wolves and livestock.

ecology↗

bioRxiv2024

Using informative priors to account for identifiability issues in occupancy models with identification errors

Non-invasive monitoring techniques like camera traps, autonomous recording units and environmental DNA are increasingly used to collect data for understanding species distribution. These methods have prompted the development of statistical models to suit specific sampling designs and get reliable ecological inferences. Site occupancy models estimate species occurrence patterns, accounting for the possibility that the target species may be present but unobserved. Here, two key processes are crucial: detection, when a species leaves signs of its presence, and identification where these signs are accurately recognized. While both processes are prone to error in general, wrong identifications are often considered as negligible with in situ observations. When applied to passive bio-monitoring data, characterized by datasets requiring automated processing, this second source of error can no longer be ignored as misclassifications at both steps can lead to significant biases in ecological estimates. Several model extensions have been proposed to address these potential errors. We propose an extended occupancy model that accounts for the identification process in addition to detection. Similar to other recent attempts to account for false positives, our model may suffer from identifiability issues, which usually require another source of data with perfect identification to resolve them. As an alternative when such data are unavailable, we propose leveraging existing knowledge of the identification process within a Bayesian framework by incorporating this knowledge through an informative prior. Through simulations, we compare different prior choices that encode varying levels of information, ranging from cases where no prior knowledge is available, to instances with accurate metrics on the performance of the identification, and scenarios based on generally accepted assumptions. We demonstrate that, compared to using a default prior, integrating information about the identification process as a prior reduces bias in parameter estimates. Overall, our approach mitigates identifiability issues, reduces estimation bias, and minimizes data requirements. In conclusion, we provide a statistical method applicable to various monitoring designs, such as camera trap, bioacoustics, or eDNA surveys, alongside non-invasive sampling technologies, to produce ecological outcomes that inform conservation decisions.

ecology↗

bioRxiv2024

Unravelling the effects of individual heterogeneity on connectivity estimates: Insights from Spatial Capture-Recapture modelling

Spatial Capture-Recapture (SCR) models using least-cost path distance offer an unified framework to estimate landscape connectivity and population size from individual detection data, while accounting for individual and spatial heterogeneity in space use. In our case study on the Pyrenean brown bear population, two "outliers" individuals were detected more often over very large spatial extent compared to other individuals. The integration of such individuals made SCR modelling challenging, especially since it remains unclear how unmodelled heterogeneity in space use may bias connectivity and population size estimates. To address this gap, we used simulations reflecting the Pyrenean brown bear population, with two groups of individuals differing in their space use due to individual characteristics but also in their spatial responses to landscape structure. We compared six SCR model formulations that varied in whether individual and spatial heterogeneity in space use were (1) ignored, (2) explicitly modelled, or (3) handled by removing outliers. We then applied the same models to the empirical bear dataset. By combining simulations and sensitivity analyses, we highlight the challenges of choosing the appropriate modelling approach when multiple sources of heterogeneity in space use occur simultaneously. Our results show that the treatment of heterogeneity in space use should match the research objective: removing outliers supports accurate population size estimation, while explicitly modelling heterogeneity is essential for reliable connectivity assessment. Overall, we provide a practical framework for identifying and addressing heterogeneity in SCR applications, to guide practitioners in deciding when additional model complexity is warranted.

ecology↗

bioRxiv2023

Analysing biodiversity observation data collected in continuous time: Should we use discrete or continuous-time occupancy models?

O_LIBiodiversity monitoring is undergoing a revolution, with fauna observations data being increasingly gathered continuously over extended periods, through sensors like camera traps and acoustic recorders, or via opportunistic observations. These data are often analysed with discrete-time ecological models, requiring the transformation of continuously collected data into arbitrarily chosen non-independent discrete time intervals. To overcome this issue, ecologists are increasingly turning to the existing continuous-time models in the literature. Closer to the real detection process, they are lesser known than discrete-time models, not always easily accessible, and can be more complex. Focusing on occupancy models, a type of species distribution models, we asked ourselves: Should we dedicate time and effort to learning and using these continuous-time models, or can we go on using discrete-time models? C_LIO_LIWe conducted a comparative simulation study using data generated within a continuous-time framework. We assessed the performance of five static occupancy models with varying detection processes: discrete detection/non-detection process, discrete count process, continuous-time Poisson process, and two types of modulated Poisson processes. Our goal was to assess their abilities to estimate occupancy probability with continuously collected data. We applied all models to empirical lynx data as an illustrative example. C_LIO_LIIn scenarios with easily detectable animals, we found that all models accurately estimated occupancy. All models reached their limits with highly elusive animals. Variation in discretisation intervals had minimal impact on the discrete models capacity to estimate occupancy accurately. C_LIO_LIOur study underscores that opting for continuous-time models with an increased number of parameters, aiming to get closer to the sensor detection process, may not offer substantial advantages over simpler models when the sole aim is to accurately estimate occupancy. Model choice can thus be driven by practical considerations such as data availability or implementation time. However, occupancy models can encompass goals beyond estimating occupancy probability. Continuous-time models, particularly those considering temporal variations in detection, can offer valuable insights into specific species behaviour and broader ecological inquiries. We hope that our findings offer valuable guidance for researchers and practitioners working with continuously collected data in wildlife monitoring and modelling. C_LI

ecology↗

bioRxiv2023

Abundance of mega-carcasses does not profoundly affect trends in the population dynamics of a facultative scavenger predator and its prey

The interplay between facultative scavenging and predation has gained interest in the last decade. The prevalence of scavenging induced by the availability of large carcasses may modify predator density or behaviour, potentially affecting prey. In contrast to behavioural mechanisms through which scavenging affects predation, the demographic effects of facultative scavenging on predator and prey populations remain poorly studied. We used the semi-natural experimental opportunity in Hwange National Park, Zimbabwe, where contrasted management measures (culling and artificial supply of water) have led to fluctuations in elephant carrion abundance, to identify the consequences of facultative scavenging on the population dynamics of a large mammalian carnivore, the spotted hyaena (Crocuta crocuta), and its prey. Using a 50-year dataset and Multivariate Autoregressive State Space models, we estimated hyaena and prey densities over four periods contrasted in elephant carrion availability due to management practices. Models that allow hyaena and their prey populations growth rate to vary depending on these four periods contributed significantly to explain variations in their density, which is consistent with an effect of management measures on the population dynamics of hyaena and its prey. Although our results support a predominant role of bottom-up mechanisms, whereby hyaena density is driven by herbivore density, itself driven by resources availability, some subtle patterns of densities could be interpreted as consequences of changes in predation pressure following changes in scavenging opportunities. We discuss why signals of prey and predator population dynamics decoupling are less likely to be observed in systems with a high diversity of prey, such as African savannas, and why inputs of mega-carcasses as pulsed resources hardly impacted top-down relationships in the long run. This study represents a first investigation of the long-term effects of carrion pulses, whose frequency may increase with climate changes, on the classical predator-prey coupling for large mammals.

ecology↗

bioRxiv2023

Bringing circuit theory into spatial occupancy models to assess landscape connectivity

Connectivity shapes species distribution across fragmented landscapes. Assessing landscape resistance to dispersal is challenging because dispersal events are rare and difficult to detect especially for elusive species. To address these issues, spatial occupancy models have been developed to integrate the resistance surface concept of landscape ecology and model patch occupancy dynamics through colonization and extinction while accounting for imperfect species detection. However, the most recent approach is based on least-cost path distances which assume that individuals disperse along the optimal route. Here, we develop a new spatial occupancy model that incorporates commute distances derived from circuit theory to model dispersal across sites. Our approach allows for explicit estimation of landscape connectivity and direct measure of uncertainty from detection/non-detection data. To illustrate our approach, we study the recolonisation of two carnivores in France, and quantify the degree to which rivers facilitate Eurasian otter (Lutra lutra) dispersal and highways impede Eurasian lynx (Lynx lynx) recolonisation. Overall, spatial occupancy models provide a flexible framework to acccommodate any distance metric designed to align with species dispersal ecology. Open Research StatementData and code used in this research are available on Zenodo at https://zenodo.org/record/8376577

ecology↗

bioRxiv2023

BOTTOM-UP EFFECTS AND DENSITY DEPENDENCE DRIVE THE DYNAMIC OF AN ANTARCTIC SEABIRD PREDATOR-PREY SYSTEM

Understanding how populations respond to variability in environmental conditions and interspecific interactions is one of the biggest challenges of population ecology, particularly in the context of global change. Although several studies have investigated population responses to climate change, very few have explicitly integrated interspecific relationships when studying these responses. Here, we aim to understand the combined effects of inter- and intraspecific interactions and environmental conditions on the demographic parameters of a prey-predator system of three sympatric seabird populations breeding in Antarctica: the south polar skua (Catharacta maccormicki), and its two main preys during the breeding season, the Adelie penguin (Pygoscelis adeliae) and the emperor penguin (Aptenodytes forsteri). We built a two-species integrated population model (IPM) with 31 years of capture-recapture and count data, and provided a framework that allows estimating demographic parameters and abundance of a predator-prey system in a context where capture-recapture data are not available for one species. Our results showed that predator-prey interactions and local environmental conditions affect differently south polar skuas depending on their breeding state of the previous year. Concerning prey-predator relationships, the number of Adelie penguin breeding pairs showed a positive effect on south polar skua survival and breeding probability, and the number of emperor penguin dead chicks showed a positive effect on the breeding success of south polar skuas. In contrast, there was no evidence for an effect of the number of south polar skuas on the demography of Adelie penguins. We also found an important impact of sea ice conditions on both the dynamics of south polar skuas and Adelie penguins. Our results suggest that this prey-predator system is mostly driven by bottom-up processes and local environmental conditions.

ecology↗

bioRxiv2022

Transnational non-invasive monitoring and spatial capture-recapture models reveal strong effects of humans on connectivity for the Pyrenean brown bear

Connectivity, in the sense of the persistence of movements between habitat patches, is key to maintain endangered populations and has to be evaluated in management plans. In practice, connectivity is difficult to quantify especially for rare and elusive species. Here, we use spatial capture-recapture (SCR) models with an ecological detection distance to identify barriers to movement. We focused on the transnational critically endangered Pyrenean brown bear (Ursus arctos) population, which is distributed over Spain, France and Andorra and is divided into two main cores areas following translocations. We integrate structured monitoring from camera traps and hair snags with opportunistic data gathered after depredation events. While structured monitoring focuses on areas of regular bear presence, the integration of opportunistic data allows us to obtain information in a wider range of habitat, which is especially important for ecological inference. By estimating a resistance parameter from encounter data, we show that the road network impedes movements, leading to smaller home ranges with increasing road density. Although the quantitative effect of roads is context-dependent (i.e. varying according to landscape configuration), our model predicts that a brown bear with a home range located in an area with relatively high road density (8.29km/km2) has a home range size reduced by 1.4-fold for males and 1.6-fold for females compared to a brown bear with a home range located in an area with low road density (1.38km/km2). When assessing connectivity, spatial capture-recapture modeling offers an alternative to the use of experts opinion when telemetry data are not available.

ecology↗

bioRxiv2022

The DeepFaune initiative: a collaborative effort towards the automatic identification of the French fauna in camera-trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequence of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera-trap images.

ecology↗

bioRxiv2021

Estimating abundance of a recovering transboundary brown bear population with capture-recapture models

AO_SCPLOWBSTRACTC_SCPLOWEstimating the size of small populations of large mammals can be achieved via censuses, or complete counts, of recognizable individuals detected over a time period: minimum detected (population) size (MDS). However, as a population grows larger and its spatial distribution expands, the risk of under-estimating population size using MDS rapidly increases because the assumption of perfect detection of all individuals in the population is violated. The need to report uncertainty around population size estimates consequently becomes crucial. We explored these biases using the monitoring framework of the critically endangered Pyrenean brown bear that was close to extinction in the mid-1990s, with only five individuals remaining, but was subsequently bolstered by the introduction of 11 bears from Slovenia. Each year since 1996, the abundance of the population has been assessed using MDS and minimum retained (population) size (MRS), which corresponded to a reassessment of the MDS in the light of the new information collected in subsequent years (e.g., adding bears which were not detected the previous years but detected the current year). We used Pollocks closed robust design (PCRD) capture-recapture models applied to the cross-border non-invasive sampling data from France, Spain and Andorra to provide the first published annual abundance and temporal trend estimates of the Pyrenean brown bear population since 2008. Annual population size increased fivefold between 2008 and 2020, going from 13 to 66 individuals. PCRD estimates were globally close to MRS counts and had reasonably narrow associated 95% Credibility Intervals. Even in cases where sampling effort is large compared to population size, the PCRD estimates of population size can diverge from the MDS counts. We report individual heterogeneity in detection that might stem from intraspecific home range size variation that result in individuals that move the most being most likely to be detected. We also found that cubs had a higher mortality rate than adults and subadults, because of infanticide by males, predation, maternal death, or abandonment. Overall, the PCRD capture-recapture modelling approach provides estimates of abundance and demographic rates of the Pyrenean brown bear population, together with associated uncertainty, while minimizing bias due to inter-individual heterogeneity in detection probabilities. We strongly encourage wildlife ecologists and managers to use robust approaches when researching large mammal populations. Such information is vital for informing management decision-making and assessing population conservation status.

ecology↗

bioRxiv2021

Eurasian lynx populations in Western Europe: What prospects for the next 50 years?

Persistence of populations may be uncertain for large carnivore species, especially for those established in human-dominated landscapes. Here, we studied the Eurasian lynx in Western Europe established in the Upper Rhine meta-population (i.e., Jura, Vosges-Palatinian and Black Forest populations) and the Alpine population. These populations are currently considered endangered or critically endangered due to high anthropogenic mortality, small population size and low genetic diversity, and isolation. We assessed lynx persistence over a 50-year time horizon by implementing a spatially-explicit individual-based model, while accounting for road mortality and habitat selection. Forecasts showed a steady growth rapidly reaching a more stable phase for the Alpine and Jura populations, and a more heterogeneous positive growth with less precision for the Vosges-Palatinian and Black Forest populations. Exchanges of individuals between populations were limited, the Jura population playing the role of a crossroad. Finally, the persistence of lynx in Western Europe seems likely on a large scale over the next 50 years. Indeed, simulations showed high female occupancy as well as average lynx density over the core areas of the four studied populations. Nevertheless, these results should be interpreted with the model limitations in mind, concerning the absence of movement barriers and inbreeding depression.

ecology↗

bioRxiv2021

Spatial integrated models foster complementarity between monitoring programs in producing large-scale ecological indicators

Over the last decades, large-scale ecological projects have emerged that require collecting ecological data over broad spatial and temporal coverage. Yet, obtaining relevant information about large-scale population dynamics from a single monitoring program is challenging, and often several sources of data, possibly heterogeneous, need to be integrated. In this context, integrated models combine multiple data types into a single analysis to quantify population dynamics of a targeted population. When working at large geographical scales, integrated spatial models have the potential to produce spatialised ecological estimates that would be difficult to obtain if data were analysed separately. In this paper, we illustrate how spatial integrated modelling offers a relevant framework for conducting ecological inference at large scales. Focusing on the Mediterranean bottlenose dolphins (Tursiops truncatus), we combined 21,464 km of photo-identification boat surveys collecting spatial capture-recapture data with 24,624 km of aerial line-transect following a distance-sampling protocol. We analysed spatial capture-recapture data together with distance-sampling data to estimate abundance and density of bottlenose dolphins. We compared the performances of the distance sampling model and the spatial capture-recapture model fitted independently, to our integrated spatial model. The outputs of our spatial integrated models inform bottlenose dolphin ecological status in the French Mediterranean Sea and provide ecological indicators that are required for regional scale ecological assessments like the EU Marine Strategy Framework Directive. We argue that integrated spatial models are widely applicable and relevant to conservation research and biodiversity assessment at large spatial scales.

ecology↗