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opencc-zeroDec 2013View details →

Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal

We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.

Data from: A generalizable energetics-based model of avian migration to facilitate continental-scale waterbird conservation

Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a Mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental-scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 d of flight with a mean seasonal survivorship of 90.5% (95% CI = 89.2%, 91.9%), whereas spring migration took a mean of 23.5 d of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry, and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including the Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.

Data from: Can collective memories shape fish distributions? A test, linking space-time occurrence models and population demographics

Social learning can be fundamental to cohesive group living, and schooling fishes have proven ideal test subjects for recent work in this field. For many species, both demographic factors, and inter- (and intra-) generational information exchange are considered vital ingredients in how movement decisions are reached. Yet key information is often missing on the spatial outcomes of such decisions, and questions concerning how migratory traditions are influenced by collective memory, density-dependent and density-independent processes remain open. To explore these issues, we focused on Atlantic herring (Clupea harengus), a long-lived, dense-schooling species of high commercial importance, noted for its unpredictable shifts in winter distribution, and developed a series of Bayesian space-time occurrence models to investigate wintering dynamics over 23 years, using point-referenced fishery and survey records from Icelandic waters. We included covariates reflecting local-scale environmental factors, temporally-lagged prey biomass and recent fishing activity, and through an index capturing distributional persistence over time, derived two proxies for spatial memory of past wintering sites. The previous winter's occurrence pattern was a strong predictor of the present pattern, its influence increasing with adult population size. Although the mechanistic underpinnings of this result remain uncertain, we suggest that a 'wisdom of the crowd' dynamic may be at play, by which navigational accuracy towards traditional wintering sites improves in larger and/or denser, better synchronized schools. Wintering herring also preferred warmer, fresher, moderately stratified waters of lower velocity, close to hotspots of summer zooplankton biomass, our results indicative of heightened environmental sensitivity in younger cohorts. Incorporating spatiotemporal correlation structure and time-varying regression coefficients improved model performance, and validation tests on independent observations one-year ahead illustrate the potential of uniting demographic information and non-stationary models to quantify both the strength of collective memory in animal groups and its relevance for the spatial management of populations.

opencc-zeroDec 2017View details →

Data from: Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)

A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species' and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co‐occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co‐occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co‐occurrence patterns into components describing shared environmental responses and residual patterns of co‐occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co‐occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co‐occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.

opencc-zeroDec 2014View details →

Data from: Predictive modelling of habitat selection by marine predators with respect to the abundance and depth distribution of pelagic prey

1. Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey. 2. We analysed contemporaneous data on the diving locations of two seabird species, the shallow-diving Peruvian Booby (Sula variegata) and deeper diving Guanay Cormorant (Phalacrocorax bougainvilliorum), and the abundance and depth distribution of their main prey, Peruvian anchoveta (Engraulis ringens). Based on this unique data set, we developed resource selection functions to test the hypothesis that the probability of seabird diving behaviour at a given location is a function of the relative abundance of prey in the upper water column. 3. For both species, we show that the probability of diving behaviour is mostly explained by the distribution of prey at shallow depths. While the probability of diving behaviour increases sharply with prey abundance at relatively low levels of abundance, support for including abundance in addition to the depth distribution of prey is weak, suggesting that prey abundance was not a major factor determining the location of diving behaviour during the study period. 4. The study thus highlights the importance of the depth distribution of prey for two species of seabird with different diving capabilities. The results complement previous research that points towards the importance of oceanographic processes that enhance the accessibility of prey to seabirds. The implications are that locations where prey is predictably found at accessible depths may be more important for surface foragers, such as seabirds, than locations where prey is predictably abundant. 5. Analysis of the relative importance of abundance and accessibility is essential for the design and evaluation of effective management responses to reduced prey availability for seabirds and other top predators in marine systems.

opencc-zeroMar 2020View details →

Data from: Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues

Cancer cells are known to exhibit unusual metabolic activity and yet, few metabolic cancer driver genes are known. Genetic alterations and epigenetic modi cations of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared to normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-speci fic cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top ranked one-hit dysregulations and 17 of the top ranked two-hit oncogenes with high similarity ratios to the templates. According literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer.

Data from: Multi-DICE: R package for comparative population genomic inference under hierarchical co-demographic models of independent single-population size changes

Population genetic data from multiple taxa can address comparative phylogeographic questions about community-scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co-demographic models that directly test multi-taxa hypotheses within a single, unified analysis while benefiting in statistical power from aggregating datasets. This approach has been applied to classical phylogeographic datasets such as mitochondrial barcodes as well as reduced-genome polymorphism datasets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD-seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest, and composite likelihood optimization. Here, we introduce the R package Multi-DICE, a wrapper program that exploits existing simulation software for straight-forward and flexible execution of hierarchical model-based inference using the aSFS, which is derived from genomic-scale data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding event pulses, and specifying flexible hyperprior distributions. In sum, Multi-DICE provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a valuable tool for comparative phylogeography and population genomics.

Data from: A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions

Terrestrial plants assimilate anthropogenic CO2 through photosynthesis and synthesizing new tissues. However, sustaining these processes requires plants to compete with microbes for soil nutrients, which therefore calls for an appropriate understanding and modeling of nutrient competition mechanisms in Earth System Models (ESMs). Here, we survey existing plant-microbe competition theories and their implementations in Earth System Models (ESMs). We found no consensus regarding the representation of nutrient competition and that observational and theoretical support for current implementations are weak. To reconcile this situation, we applied the Equilibrium Chemistry Approximation (ECA) theory to plant-microbe nitrogen competition in a detailed grassland 15N tracer study and found that competition theories in current ESMs fail to capture observed patterns and the ECA prediction simplifies the complex nature of nutrient competition and quantitatively matches the 15N observations. Since plant carbon dynamics are strongly modulated by soil nutrient acquisition, we conclude that (1) predicted nutrient limitation effects on terrestrial carbon accumulation by existing ESMs may be biased and (2) our ECA-based approach may improve predictions by mechanistically representing plant-microbe nutrient competition.

Data from: Variation in growth drives the duration of parental care: a test of Ydenberg's model

The duration of parental care in animals varies widely, from none to lifelong. Such variation is typically thought to represent a trade-off between growth and safety. Seabirds show wide variation in the age at which offspring leave the nest, making them ideal to test the idea that a trade-off between high energy gain at sea and high safety at the nest drives variation in departure age (Ydenberg's model). To directly test the model assumptions, we attached time-depth recorders to murre parents (fathers [which do all parental care at sea] and mothers; of each). Except for the initial mortality experienced by chicks departing from the colony, the mortality rate at sea was similar to the mortality rate at the colony. However, energy gained by the chick per day was ∼2.1 times as high at sea compared with at the colony because the father spent more time foraging, since he no longer needed to spend time commuting to and from the colony. Compared with the mother, the father spent ∼2.6 times as much time diving per day and dived in lower-quality foraging patches. We provide a simple model for optimal departure date based on only (1) the difference in growth rate at sea relative to the colony and (2) the assumption that transition mortality from one life-history stage to the other is size dependent. Apparently, large variation in the duration of parental care can arise simply as a result of variation in energy gain without any trade-off with safety.

opencc-zeroDec 2013View details →

Data from: Modelling the dispersal of the two main hosts of the raccoon rabies variant in heterogeneous environments with landscape genetics

Predicting the geographic spread of wildlife epidemics requires knowledge about the movement patterns of disease hosts or vectors. The field of landscape genetics provides valuable approaches to study dispersal indirectly, which in turn may be used to understand patterns of disease spread. Here, we applied landscape genetic analyses and spatially explicit models to identify the potential path of raccoon rabies spread in a mesocarnivore community. We used relatedness estimates derived from microsatellite genotypes of raccoons and striped skunks to investigate their dispersal patterns in a heterogeneous landscape composed predominantly of agricultural, forested and residential areas. Samples were collected in an area covering 22 000 km2 in southern Québec, where the raccoon rabies variant (RRV) was first detected in 2006. Multiple regressions on distance matrices revealed that genetic distance among male raccoons was strictly a function of geographic distance, while dispersal in female raccoons was significantly reduced by the presence of agricultural fields. In skunks, our results suggested that dispersal is increased in edge habitats between fields and forest fragments in both males and females. Resistance modelling allowed us to identify likely dispersal corridors used by these two rabies hosts, which may prove especially helpful for surveillance and control (e.g. oral vaccination) activities.

Data from: Spatio-temporal modelling of auk abundance after the Erika oil spill and implications for conservation

Species distribution models are widely used in applied ecology and conservation. While accounting for spatial dependences is now the rule, temporal dependences have rarely been dealt with explicitly. In this study, we analyse wintering auk distribution in the Bay of Biscay and English Channel and estimate changes in abundance within and between years while accounting for space–time dependencies. We then propose a retrospective estimate of the impact of the Erika oil spill that occurred in December 1999. Two series of extensive aerial surveys, repeated at intervals of 1–2 months, were carried out at a 10-year interval off the French Atlantic coast (2001–2002 and 2011–2012). Spatially and temporally explicit Bayesian models were fitted to these data to provide spatio-temporal predictions of auk abundance. These were then used to compare abundances within the area affected by the Erika oil spill two and twelve years after the catastrophe. The results showed that 1·55 million auks wintered in the study area in 2011–2012. The main wintering area was the English Channel (more than one million auks) but the Bay of Biscay also became an important area in the middle of winter (470 000 auks) owing to a strong southward shift in auk distribution. Two years after the catastrophe (2001–2002), the area affected by the Erika oil spill hosted a small proportion of auks of the Bay of Biscay – about 80 000 individuals. This number increased by more than three times 10 years later and reached 270 000 individuals, whereas no significant change was detected elsewhere. We suggest that it could result from a recovery after the extra-mortality induced by the Erika oil spill. Policy implications. This study identified major auk wintering areas, with abundances much higher than previously realized. Oil spills have occurred regularly in these areas, with major delayed impacts on auk breeding populations. The worst case scenario would be if a major oil spill occurred in the English Channel in February, when abundance reaches one million auks. Although such a disaster has not so far occurred, stricter policies on the transport of hydrocarbons should be implemented to prevent such a possibility.

opencc-zeroDec 2012View details →

Data from: Scale-dependent foraging ecology of a marine top predator modelled using passive acoustic data

1. Understanding which environmental factors drive foraging preferences is critical for the development of effective management measures, but resource use patterns may emerge from processes that occur at different spatial and temporal scales. Direct observations of foraging are also especially challenging in marine predators, but passive acoustic techniques provide opportunities to study the behavior of echolocating species over a range of scales. 2. We used an extensive passive acoustic dataset to investigate the distribution and temporal dynamics of foraging in bottlenose dolphins using the Moray Firth (Scotland, UK). Echolocation buzzes were identified with a mixture model of detected echolocation inter-click intervals, and used as a proxy of foraging activity. A robust modelling approach accounting for autocorrelation in the data was then used to evaluate which environmental factors were associated with the observed dynamics at two different spatial and temporal scales. 3. At a broad scale, foraging varied seasonally, and was also affected by sea-bed slope and shelf-sea fronts. At a finer scale, we identified variation in seasonal use and local interactions with tidal processes. Foraging was best predicted at a daily scale, accounting for site-specificity in the shape of the estimated relationships. 4. This study demonstrates how passive acoustic data can be used to understand foraging ecology in echolocating species, and provides a robust analytical procedure for describing spatio-temporal patterns. Associations between foraging and environmental characteristics varied according to spatial and temporal scale, highlighting the need for a multi-scale approach. Our results indicate that dolphins respond to coarser-scale temporal dynamics, but have a detailed understanding of finer-scale spatial distribution of resources.

Data from: A model-derived short-term estimation method of effective size for small populations with overlapping generations

If not actively managed, small and isolated populations lose their genetic variability and the inbreeding rate increases. Combined, these factors limit the ability of populations to adapt to environmental changes, increasing their risk of extinction. The effective population size (Ne) is proportional to the loss of genetic diversity and therefore of considerable conservation relevance. However, estimators of Ne that account for demographic parameters in species with overlapping generations require sampling of populations across generations, which is often not feasible in long-lived species. We created an individual-based model that allows calculation of Ne based on demographic parameters that can be obtained in a time period much shorter than a generation. It can be adapted to every life-history parameter combination. The model is freely available as an r-package NEff. The model was first used in a simulation experiment observing changes in Ne in response to different degrees of generational overlap. Results showed that increased generational overlap slowed annual rates of heterozygosity loss, resulting in higher annual effective sizes (Ny) but decreased Ne per generation. Adding the effect of different recruitment rates only affected Ne for populations with low generational overlap. The model was further tested using real population data of the Australian arboreal gecko Gehyra variegata. Simulation results were compared to genetic analyses and matched estimates of the real population very well. Unlike other estimation methods of Ne, NEff neither requires long time series of population monitoring nor genetic analyses of changes in gene frequencies. Thus, it seems to be the first method for calculating Ne within short time periods and comparably low costs facilitating the use of Ne in applied conservation and management.

opencc-zeroOct 2019View details →

Data from: Modelling misclassification in multi-species acoustic data when estimating occupancy and relative activity

<p>1. Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) can efficiently gather community data for a variety of taxa, but generally require software algorithms to classify each recorded call to a species. Species classification errors are possible during this process and result in both false negative and false positive detections. Available approaches for analysing ARU data do not model the species classification probabilities, meaning erroneous detections are attributed to an omnibus source instead of the presence of another species. Additionally, counts of call recordings for each species are often summarized to binary detection data for analyses. Expanding statistical models to capture these nuances of ARU data would allow for improved inferences about occupancy and relative activity. 2. Motivated by bat acoustic surveys, we developed a model to analyse counts of call recordings from multiple species simultaneously while accounting for species classification errors. Our model expands on previously developed false positive occupancy models to better describe acoustic data. We used simulations to compare our model to other false positive occupancy models for an example scenario with ARU data from two species. We also analyse acoustic data for eight bat species in Montana using our model. 3. In simulations, single-species models resulted in biased estimates of occupancy and relative activity because they failed to associate false positives with the presence of the second species. Models analysing binary observations ignored available information on relative activity and led to less precise estimates. Applying our model to bat acoustic data from Montana allowed for species-specific estimates of occupancy and relative activity. This analysis illustrates the flexibility in our model framework while also highlighting the assumptions and data requirements for implementation. Specifically, additional information on the species classification probabilities is needed and we discuss considerations for reliably estimating these parameters. 4. Directly modelling the species classification probabilities allows for improved ecological inferences for both occupancy and relative activity using community ARU data. Our statistical framework helps address the challenges posed by acoustic data, allowing ecologists to better utilize this technology to monitor wildlife communities.</p>

opencc-zeroDec 2011View details →

Data from: Predation's role in life-history evolution of a livebearing fish and a test of the Trexler-DeAngelis model of maternal provisioning

Populations experiencing consistent differences in predation risk and resource availability are expected to follow divergent evolutionary trajectories. For example, live-history theory makes specific predictions for how predation should drive life-history evolution, and according to the Trexler-DeAngelis model for the evolution of matrotrophy, post-fertilization maternal provisioning is most likely to evolve in environments with consistent, high levels of resource availability. Using the model system of Bahamas mosquitofish (Gambusia hubbsi) inhabiting blue holes with and without the piscivorous bigmouth sleeper (Gobiomorus dormitor), we provide some of the strongest tests of these predictions to date, as resource availability does not co-vary with predation regime in this system, and we examine numerous (14) isolated natural populations. We found clear evidence for the expected life-history divergence between predation regimes, and empirical support of the Trexler-DeAngelis model. Moreover, based on molecular and lab-rearing data, our study offers strong evidence for convergent evolution of similar life histories in similar predation regimes, largely matching previous phenotypic patterns observed in other poeciliid lineages (Brachyrhaphis spp., Poecilia reticulata), and further supports the notion that matrotrophy is most likely to evolve in stable high-resource environments.

opencc-zeroDec 2010View details →

Data from: Are pollination "syndromes" predictive? Asian dalechampia fit neotropical models

Using pollination-syndrome parameters and pollinator correlations with floral phenotype from the neotropics, we predicted that Dalechampia bidentata Blume (Euphorbiaceae) in southern China would be pollinated by female resin-collecting bees between 12 and 20 mm in length. Observations in southwestern Yunnan Province, China, revealed pollination by resin-collecting, female Megachile (Callomegachile) nr. faceta Bingham (Hymenoptera: Megachilidae). These bees, at 14 mm in length, were in the predicted size range, confirming the utility of syndromes and models developed in distant regions. Phenotypic-selection analyses and estimation of adaptive surfaces and adaptive accuracies together suggest that the blossoms of D. bidentata are well adapted to pollination by their most common floral visitors.

opencc-zeroDec 2011View details →

Data from: Coalescent and biophysical models of stepping-stone gene flow in Neritid snails

Marine species in the Indo-Pacific have ranges that can span thousands of kilometers, yet studies increasingly suggest that mean larval dispersal distances are less than historically assumed. Gene flow across these ranges must therefore rely to some extent on larval dispersal among intermediate "stepping-stone" populations in combination with long-distance dispersal far beyond the mean of the dispersal kernel. We evaluate the strength of stepping-stone dynamics by employing a spatially explicit biophysical model of larval dispersal in the Tropical Pacific to construct hypotheses for dispersal pathways. We test these hypotheses with coalescent models of gene flow among high-island archipelagos in four Neritid gastropod species. Two of the species live in the marine intertidal, while the other two are amphidromous, living in freshwater but retaining pelagic dispersal. Dispersal pathways predicted by the biophysical model were strongly favored in 16 of 18 tests against alternate hypotheses. In regions where connectivity among high-island archipelagos was predicted as direct, there was no difference in gene flow between marine and amphidromous species. In regions where connectivity was predicted through stepping-stone atolls only accessible to marine species, gene flow estimates between high-island archipelagos were significantly higher in marine species. Moreover, one of the marine species showed a significant pattern of isolation-by-distance consistent with stepping-stone dynamics. While our results support stepping-stone dynamics in Indo-Pacific species, we also see evidence for non-equilibrium processes such as range expansions or rare long-distance dispersal events. This study provides an empirical assessment of a biophysical model that helps to shed light on larval dispersal pathways.

Data from: 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 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. Here, we focused on wolves (Canis lupus) that have 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 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 2500m and positively influenced by the number of observed occupied sites at short and longdistances , 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 decisionmakers to target sites where large carnivores are likely to occur and mitigate conflicts.

Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model

Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.