The Experts below are selected from a list of 9234 Experts worldwide ranked by ideXlab platform
Olivier Gimenez - One of the best experts on this subject based on the ideXlab platform.
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mapping and explaining wolf recolonization in france using dynamic occupancy models and opportunistic data
Ecography, 2018Co-Authors: Julie Louvrier, Remi Choquet, Eric Marboutin, Christophe Duchamp, Sarah Cubaynes, Christian Miquel, Valentin Lauret, Olivier GimenezAbstract: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 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. This article is protected by copyright. All rights reserved.
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mapping and explaining wolf recolonization in france using dynamic occupancy models and opportunistic data
bioRxiv, 2017Co-Authors: Julie Louvrier, Remi Choquet, Eric Marboutin, Christophe Duchamp, Sarah Cubaynes, Christian Miquel, Olivier GimenezAbstract:While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. 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 90s. 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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improving abundance estimation by combining capture recapture and occupancy data example with a large carnivore
Journal of Applied Ecology, 2014Co-Authors: Eric Marboutin, Laetitia Blanc, Sylvain Gatti, Fridolin Zimmermann, Olivier GimenezAbstract:Summary 1. Abundance is a key quantity for conservation and management strategies but remains challenging to assess in the field. Capture–recapture (CR) methods are often used to estimate abundance while correcting for Imperfect Detection, but these methods are costly. Occupancy, sometimes considered as a surrogate for abundance, is estimated through the collection of presence/absence data and is less costly while allowing gathering of information at a large spatial scale. 2. Building on the recent pieces of work on the combination of different data sources, we showed how abundance data can be complemented by presence/absence data and can be analysed conjointly to improve abundance estimates. Our approach relies on a hierarchical model that makes explicit the link between the abundance and occupancy state variables while formally accounting for Imperfect Detection. 3. We used a population of Eurasian lynx in France monitored via camera traps and a collection of presence signs as an illustration of our approach. 4. Synthesis and applications. We combined capture–recapture and occupancy data and demonstrated that we can efficiently improve abundance estimates. Our method can be used by managers when estimates of trends in abundance lack power due to sparse data collected during an intensive survey, by simply integrating data collected during non-systematic survey. Furthermore, combining these two sampling procedures makes full use of all available data and allows the development of conservation and management strategies based on precise abundance estimates. Overall, the combination of different data sources in an integrated statistical framework has great potential, especially for elusive species.
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age specific cost of first reproduction in female southern elephant seals
Biology Letters, 2014Co-Authors: Marine Desprez, Olivier Gimenez, Sarah Cubaynes, Robert Harcourt, Mark A Hindell, Clive R McmahonAbstract:When to commence breeding is a crucial life-history decision that may be the most important determinant of an individual's lifetime reproductive output and can have major consequences on population dynamics. The age at which individuals first reproduce is an important factor influencing the intensity of potential costs (e.g. reduced survival) involved in the first breeding event. However, quantifying age-related variation in the cost of first reproduction in wild animals remains challenging because of the difficulty in reliably recording the first breeding event. Here, using a multi-event capture–recapture model that accounts for both Imperfect Detection and uncertainty in the breeding status on an 18-year dataset involving 6637 individuals, we estimated age and state-specific survival of female elephant seals (Mirounga leonina) in the declining Macquarie Island population. We detected a clear cost of first reproduction on survival. This cost was higher for both younger first-time breeders and older first-time breeders compared with females recruiting at age four, the overall mean age at first reproduction. Neither earlier primiparity nor delaying primiparity appear to confer any evolutionary advantage, rather the optimal strategy seems to be to start breeding at a single age, 4 years.
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an index of risk of co occurrence between marine mammals and watercraft example of the florida manatee
Biological Conservation, 2013Co-Authors: Sarah Bauduin, Stacie M Koslovsky, Holly H Edwards, Olivier Gimenez, Julien Martin, Daniel E FaganAbstract:Collisions between wildlife and vehicles represent a large source of mortality for many species. To implement effective protection zones, it is important to identify areas in which wildlife–vehicle collisions are likely to occur. We used statistical models to derive an index of risk of co-occurrence between manatees and boats. Our statistical models were used to predict the distribution of both manatees and boats, while accounting for observer-specific Detection probabilities. Models used aerial survey data and we found that both environmental and temporal covariates influenced manatee and boat distributions. Moreover, the probability of detecting manatees varied substantially with the weather and among observers. To our knowledge, this is the first time that manatee distribution is modeled as a function of key environmental and seasonal covariates, while accounting for Imperfect Detection of manatees. We computed an index of risk of co-occurrence by multiplying the probability of manatee occupancy by the expected boat density and occupancy to identify areas where manatee–boat collisions are likely to occur. This analytical framework emphasizes the importance of accounting for Imperfect Detection, and how modeling distribution of both organisms and vehicles as a function of key covariates can help improve predictions of risk of collisions. Risk of collision metrics can then be used in designing protection zones.
Robert M Dorazio - One of the best experts on this subject based on the ideXlab platform.
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integrated species distribution models combining presence background data and site occupancy data with Imperfect Detection
Methods in Ecology and Evolution, 2017Co-Authors: Vira Koshkina, Robert M Dorazio, Yang Wang, A Gordon, Matt White, Lewi StoneAbstract:Summary Two main sources of data for species distribution models (SDMs) are site-occupancy (SO) data from planned surveys, and presence-background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorporates Detection probability. The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow-bellied glider (Petaurus australis) in south-eastern Australia, with the predictive performance of the Integrated Model again found to be superior. PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out-of-sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone. Unlike conventional SDMs which have restrictive scale-dependence in their predictions, our Integrated Model is based on a point process model and has no such scale-dependency. It may be used for predictions of abundance at any spatial-scale while still maintaining the underlying relationship between abundance and area.
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RESEARCH ARTICLE Environmental DNA (eDNA) Sampling Improves Occurrence and Detection Estimates of Invasive Burmese Pythons
2016Co-Authors: Margaret E Hunter, Robert M Dorazio, Sara J. Oyler-mccance, Jennifer A. Fike, J. Smith, Charles T. Hunter, Robert N. Reed, Kristen M HartAbstract:Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and Detection probabilities and thereby account for im-perfect Detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of Imperfect Detection. Low Detection of invasive giant constrictors using visual sur-veys and traps has hampered the estimation of occupancy and Detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with Detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow an-aconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to be
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Environmental DNA (eDNA) Sampling Improves Occurrence and Detection Estimates of Invasive Burmese Pythons
2015Co-Authors: Margaret E Hunter, Robert M Dorazio, Sara J. Oyler-mccance, Jennifer A. Fike, Charles T. Hunter, Robert N. Reed, Brian J. Smith, Kristen M HartAbstract:Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and Detection probabilities and thereby account for Imperfect Detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of Imperfect Detection. Low Detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and Detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with Detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of Detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative Detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models.
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accounting for Imperfect Detection and survey bias in statistical analysis of presence only data
Global Ecology and Biogeography, 2014Co-Authors: Robert M DorazioAbstract:Aim During the past decade ecologists have attempted to estimate the parameters of species distribution models by combining locations of species presence observed in opportunistic surveys with spatially referenced covariates of occurrence. Several statistical models have been proposed for the analysis of presence-only data, but these models have largely ignored the effects of Imperfect Detection and survey bias. In this paper I describe a model-based approach for the analysis of presence-only data that accounts for errors in the Detection of individuals and for biased selection of survey locations. Innovation I develop a hierarchical, statistical model that allows presence-only data to be analysed in conjunction with data acquired independently in planned surveys. One component of the model specifies the spatial distribution of individuals within a bounded, geographic region as a realization of a spatial point process. A second component of the model specifies two kinds of observations, the Detection of individuals encountered during opportunistic surveys and the Detection of individuals encountered during planned surveys. Main conclusions Using mathematical proof and simulation-based comparisons, I demonstrate that biases induced by errors in Detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence-only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high-quality data (from planned surveys) can be used to leverage the information in presence-only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence-only data is widely applicable. In addition, since the point-process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.
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ESTIMATING SPECIES RICHNESS AND ACCUMULATION BY MODELING SPECIES OCCURRENCE AND DETECTABILITY
Ecology, 2006Co-Authors: Robert M Dorazio, J. Andrew Royle, Bo Söderström, Anders GlimskärAbstract:A statistical model is developed for estimating species richness and accumulation by formulating these community-level attributes as functions of model-based estimators of species occurrence while accounting for Imperfect Detection of individual species. The model requires a sampling protocol wherein repeated observations are made at a collection of sample locations selected to be representative of the community. This temporal replication provides the data needed to resolve the ambiguity between species absence and nonDetection when species are unobserved at sample locations. Estimates of species richness and accumulation are computed for two communities, an avian community and a butterfly community. Our model-based estimates suggest that Detection failures in many bird species were attributed to low rates of occurrence, as opposed to simply low rates of Detection. We estimate that the avian community contains a substantial number of uncommon species and that species richness greatly exceeds the number of species actually observed in the sample. In fact, predictions of species accumulation suggest that even doubling the number of sample locations would not have revealed all of the species in the community. In contrast, our analysis of the butterfly community suggests that many species are relatively common and that the estimated richness of species in the community is nearly equal to the number of species actually detected in the sample. Our predictions of species accumulation suggest that the number of sample locations actually used in the butterfly survey could have been cut in half and the asymptotic richness of species still would have been attained. Our approach of developing occurrence-based summaries of communities while allowing for Imperfect Detection of species is broadly applicable and should prove useful in the design and analysis of surveys of biodiversity.
Marc Kery - One of the best experts on this subject based on the ideXlab platform.
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Unraveling fine-scale habitat use for secretive species: When and where toads are found when not breeding
2018Co-Authors: Karoline C. Gilioli, Marc Kery, Murilo GuimarãesAbstract:A good understanding of species-habitat associations, or habitat use, is required to establish conservation strategies for any species. Many amphibian species are elusive and most information concerning amphibian habitat use comes from breeding sites where they are comparatively easy to find and study. Knowledge about retreat sites is extremely limited for most species and for the greater part of the year. For such species, it is especially important to factor in Detection probability in habitat analyses, because otherwise distorted views about habitat preferences may result, e.g., when a species is more visible in habitat type B than in A, even though A may be preferred. The South American red-belly toad, Melanophryniscus pachyrhynus, is a range-restricted species from Southern Brazil and Uruguay that inhabits open areas with rocky outcrops and is usually seen only during explosive breeding events. Here we studied the fine-scale habitat use of the red-belly toad outside of the breeding season to identify retreat sites and test for the importance of accounting for species Imperfect Detection, using Bayesian occupancy models. We identified shrub density and the number of loose rocks as important predictors of occupancy, while Detection probability was highest at intermediate temperatures. Considering the harsh (dry and hot) conditions of rocky outcrops, shrubs and loose rocks may both work as important refuges, besides providing food resources and protecting against predation. Rocky outcrops have been suffering changes in habitat configuration and we identify nonbreeding habitat preferences at a fine scale, which may help to promote population persistence, and highlight the importance of accounting for Imperfect Detection when studying secretive species.
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Study of biological communities subject to Imperfect Detection: bias and precision of community N-mixture abundance models in small-sample situations
Ecological Research, 2016Co-Authors: Yuichi Yamaura, Marc Kery, J. Andrew RoyleAbstract:Community N -mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to Imperfect Detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance $$(\bar{\lambda }$$ ( λ ¯ : 0.1, 0.5, 1, 5), Detection probability $$(\bar{p}$$ ( p ¯ : 0.1, 0.2, 0.5), and number of sampling sites ( n _ site : 10, 20, 40) and visits ( n _ visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables $$(\bar{\lambda }$$ ( λ ¯ , $$\bar{p}$$ p ¯ , n _ site , n _ visit ) increased. Detection probability $$\bar{p}$$ p ¯ was most important for the estimates of mean abundance, while $$\bar{\lambda }$$ λ ¯ was most influential for covariate effect and species richness estimates. For all parameters, increasing n _ site was more beneficial than increasing n _ visit . Minimal conditions for obtaining adequate performance of community abundance models were n _ site ≥ 20, $$\bar{p}$$ p ¯ ≥ 0.2, and $$\bar{\lambda }$$ λ ¯ ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N -mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.
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applied hierarchical modeling in ecology analysis of distribution abundance and species richness in r and bugs
2015Co-Authors: Marc Kery, Andrew J RoyleAbstract:Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. * Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating Imperfect Detection* Presents models and methods for identifying unmarked individuals and species* Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses* Includes companion website containing data sets, code, solutions to exercises, and further information
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site occupancy models in the analysis of environmental dna presence absence surveys a case study of an emerging amphibian pathogen
Methods in Ecology and Evolution, 2013Co-Authors: Benedikt R Schmidt, Marc Kery, Sylvain Ursenbacher, Oliver J Hyman, James P CollinsAbstract:Summary 1. The use of environmental DNA (eDNA) to detect species in aquatic environments such as ponds and streams is a powerful new technique with many benefits. However, species Detection in eDNA-based surveys is likely to be Imperfect, which can lead to underestimation of the distribution of a species. 2. Site occupancy models account for Imperfect Detection and can be used to estimate the proportion of sites where a species occurs from presence/absence survey data, making them ideal for the analysis of eDNA-based surveys. Imperfect Detection can result from failure to detect the species during field work (e.g. by water samples) or during laboratory analysis (e.g. by PCR). 3. To demonstrate the utility of site occupancy models for eDNA surveys, we reanalysed a data set estimating the occurrence of the amphibian chytrid fungus Batrachochytrium dendrobatidis using eDNA. Our reanalysis showed that the previous estimation of species occurrence was low by 5–10%. Detection probability was best explained by an index of the number of hosts (frogs) in ponds. 4. Per-visit availability probability in water samples was estimated at 0� 45 (95% CRI 0� 32, 0� 58) and per-PCR Detection probability at 0� 85 (95% CRI 0� 74, 0� 94), and six water samples from a pond were necessary for a cumulative Detection probability >95%. A simulation study showed that when using site occupancy analysis, researchers need many fewer samples to reliably estimate presence and absence of species than without use of site occupancy modelling. 5. Our analyses demonstrate the benefits of site occupancy models as a simple and powerful tool to estimate Detection and site occupancy (species prevalence) probabilities despite Imperfect Detection. As species Detection from eDNA becomes more common, adoption of appropriate statistical methods, such as site occupancy models, will become crucial to ensure that reliable inferences are made from eDNA-based surveys.
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population trends of brown hares in switzerland the role of land use and ecological compensation areas
Biological Conservation, 2011Co-Authors: Judith Zellwegerfischer, Marc Kery, Gilberto PasinelliAbstract:Abstract Over the last decades, agricultural land-use practices have been intensified throughout Europe. As a consequence of the resulting loss of habitat heterogeneity, numerous species associated with traditional farmland have undergone severe population declines. To mitigate the negative effects of intensive agriculture on farmland biodiversity, agri-environment schemes (AES) have been adopted in various European countries since the early 1990s. The effects of AES have been evaluated for different taxa, but rarely for larger mammals like the brown hare ( Lepus europaeus ), a characteristic species of traditional open farmland. Using spotlight counts from 58 brown hare monitoring study sites over 17 years, we analysed the effects of land-use and several agri-environment scheme options on brown hare density in the Swiss lowland. We used open-population binomial mixture models to jointly model abundance and Detection probability, thereby accounting for Imperfect Detection of hares. Mean observed counts of brown hares in Switzerland from 1992 to 2008 suggested a slight decline followed by a recovery in arable study sites, whereas a sustained decline was apparent in grassland sites. Mean Detection probability ranged widely from year to year (arable: 0.33–0.70; grassland: 0.21–0.80). When accounting for Imperfect Detection, a population recovery was apparent in both land-use types, although hare densities remained at low levels compared to other European countries. The amount of extensively managed hay meadows seemed to have a positive effect on brown hare abundance both in arable and grassland sites. Hedgerows were also positively related to hare density, although only in arable study sites. The amount of set-asides/wildflower strips and brown hare density were related neither in arable nor in grassland sites. This result was probably caused by the fairly low percentages of this AES option in our study sites. Habitat improvements by means of AES indicate some positive effects on brown hare populations in Switzerland, but the quantity and quality of AES must still be increased. Combined with a binomial mixture model correcting for Imperfect Detection, spotlight counts are an effective tool for estimating population trends, especially for large-scale and long-term surveys like the Swiss brown hare monitoring.
Gurutzeta Guilleraarroita - One of the best experts on this subject based on the ideXlab platform.
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graphical diagnostics for occupancy models with Imperfect Detection
Methods in Ecology and Evolution, 2017Co-Authors: David I Warton, Gurutzeta Guilleraarroita, Jakub Stoklosa, Darryl I Mackenzie, A H WelshAbstract:Summary Occupancy-Detection models that account for Imperfect Detection have become widely used in many areas of ecology. As with any modelling exercise, it is important to assess whether the fitted model encapsulates the main sources of variation in the data, yet there have been few methods developed for occupancy-Detection models that would allow practitioners to do so. In this paper, a new type of residual for occupancy-Detection models is developed according to the method of Dunn & Smyth (Journal of Computational and Graphical Statistics, 5, 1996, 236–244). Residuals are separately constructed to diagnose the occupancy and Detection components of the model. Because the residuals are quite noisy, we suggest fitting a smoother through plots of residuals against predictors of fitted values, with 95% confidence bands, to diagnose lack-of-fit. The method is illustrated using Swiss squirrel data, and evaluated using simulations based on that dataset. Plotting residuals against predictors or against fitted values performed reasonably well as methods for diagnosing violations of occupancy-Detection model assumptions, particularly plots of residuals against a missing predictor. Relatively high false positive rates were sometimes observed, but this seems to be controlled reasonably well by fitting smoothers to these plots and being guided in interpretation by 95% confidence bands around the smoothers.
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is my species distribution model fit for purpose matching data and models to applications
Global Ecology and Biogeography, 2015Co-Authors: Gurutzeta Guilleraarroita, Jose J Lahozmonfort, Michael A Mccarthy, A Gordon, Jane Elith, Heini Kujala, Pia E Lentini, Reid Tingley, Brendan A WintleAbstract:Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. Imperfect Detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.
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ignoring Imperfect Detection in biological surveys is dangerous a response to fitting and interpreting occupancy models
PLOS ONE, 2014Co-Authors: Gurutzeta Guilleraarroita, Jose J Lahozmonfort, Darryl I Mackenzie, Brendan A Wintle, Michael A MccarthyAbstract:In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for Imperfect Detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding Imperfect Detection. When ignored, occupancy and Detection are confounded: the same naive occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and Detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naive occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.
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designing studies to detect differences in species occupancy power analysis under Imperfect Detection
Methods in Ecology and Evolution, 2012Co-Authors: Gurutzeta Guilleraarroita, Jose J LahozmonfortAbstract:Summary 1. Studies aimed at estimating species site occupancy while accounting for Imperfect Detection are common in ecology and conservation. Often there is interest in assessing whether occupancy differs between two samples, for example, two points in time, areas or habitats. To ensure that meaningful results are obtained in such studies, attention has to be paid to their design, and power analysis is a useful means to accomplish this. 2. We provide tools for conducting power analysis in studies aimed at detecting occupancy differences under Imperfect Detection and explore associated design trade-offs. We derive a formula in closed form that conveniently allows determining the sample size required to detect a difference in occupancy with a given power. Because this formula is based on asymptotic approximations, we use simulations to assess its performance, at the same time comparing that of different significance tests. 3. We show that the closed-formula performs well in a wide range of scenarios, providing a useful lower sample size bound. For the simulated scenarios, a Wald test on the probability scale was the most powerful test among those evaluated. 4. We found that choosing the number of repeat visits based on existing recommendations for single-season studies will often be a good approach in terms of minimizing the effort required to achieve a given power. 5. We demonstrate that our results and discussion are applicable regardless of whether independence or Markovian dependence is assumed in the occupancy status of sites between seasons, and illustrate their utility when designing to detect a trend in multiple-season studies. 6. Assessing differences in species occupancy is relevant in many ecological and conservation applications. For the outcome of these monitoring efforts to be meaningful and so to avoid wasting the often limited resources, survey design has to be carefully addressed to ensure that the relevant differences can be indeed detected and that this is achieved in the most efficient way. Here, we provide guidance and tools of immediate practical use for the design of such studies, including code to conduct power analysis.
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design of occupancy studies with Imperfect Detection
Methods in Ecology and Evolution, 2010Co-Authors: Gurutzeta Guilleraarroita, Martin S Ridout, Byron J T MorganAbstract:Summary 1. Occupancy is an important concept in ecology. To obtain an unbiased estimator of occupancy it is necessary to address the issue of Imperfect Detection, which requires conducting replicate surveys at the sites being sampled. As the allocation of total effort can be done in different ways, occupancy studies should be designed carefully to ensure an efficient use of available resources. 2. In this paper we address the design of single-season single-species occupancy studies with a focus on: (1) issues relating to small sample sizes and (2) the potential relevance of including the precision of the detectability estimator as a criterion for design. We explore analytically the model with constant probabilities and examine how bias and precision are affected by the numbers of sites and replicates used. 3. We show how, for small sample sizes, the estimator properties depart from those predicted by large sample approximations, emphasize the need to use simulations when designing for small sample sizes and provide a new software tool that can assist in this process. 4. We offer advice on the amount of replication needed when the probability of Detection is a quantity of interest and show that, in this case, it is more efficient to reduce the number of sites and increase the amount of replication per site compared with situations where only occupancy is of concern. 5. Synthesis and applications. It is essential to have clearly stated objectives before starting a study and to design the sampling accordingly. As the allocation of effort into replication and sites can be done in different ways, occupancy studies should be designed carefully to ensure an efficient use of available resources. To avoid waste, it is crucial to anticipate the quality of the estimates that can be expected from a particular study design. The discussion and guidance provided here is of special interest for those designing occupancy studies with small sample sizes, something not uncommon in the context of ecology and conservation.
Julien Martin - One of the best experts on this subject based on the ideXlab platform.
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Monitoring abundance of aggregated animals (Florida manatees) using an unmanned aerial system (UAS)
'Springer Science and Business Media LLC', 2021Co-Authors: Holly H Edwards, Jeffrey A. Hostetler, Bradley M. Stith, Julien MartinAbstract:Abstract Imperfect Detection is an important problem when counting wildlife, but new technologies such as unmanned aerial systems (UAS) can help overcome this obstacle. We used data collected by a UAS and a Bayesian closed capture-mark-recapture model to estimate abundance and distribution while accounting for Imperfect Detection of aggregated Florida manatees (Trichechus manatus latirostris) at thermal refuges to assess use of current and new warmwater sources in winter. Our UAS hovered for 10 min and recorded 4 K video over sites in Collier County, FL. Open-source software was used to create recapture histories for 10- and 6-min time periods. Mean estimates of probability of Detection for 1-min intervals at each canal varied by survey and ranged between 0.05 and 0.92. Overall, Detection probability for sites varied between 0.62 and 1.00 across surveys and length of video (6 and 10 min). Abundance varied by survey and location, and estimates indicated that distribution changed over time, with use of the novel source of warmwater increasing over time. The highest cumulative estimate occurred in the coldest winter, 2018 (N = 158, CI 141–190). Methods here reduced survey costs, increased safety and obtained rigorous abundance estimates at aggregation sites previously too difficult to monitor
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combining information for monitoring at large spatial scales first statewide abundance estimate of the florida manatee
Biological Conservation, 2015Co-Authors: Julien Martin, Stacie M Koslovsky, Christopher Fonnesbeck, Craig W Harmak, Holly H Edwards, Teri M DaneAbstract:Abstract Monitoring abundance and distribution of organisms over large landscapes can be difficult. Because of challenges associated with logistics and data analyses uncorrected counts are often used as a proxy for abundance. We present the first statewide estimate of abundance for Florida manatees (Trichechus manatus latirostris) using an innovative approach that combines multiple sources of information. We used a combination of a double-observer protocol, repeated passes, and collection of detailed diving behavior data to account for Imperfect Detection of animals. Our estimate of manatee abundance was 6350 (95%CI: 5310–7390). Specifically, we estimated 2790 (95%CI: 2160–3540) manatees on the west coast (2011), and 3560 (95%CI: 2850–4410) on the east coast (2012). Unlike uncorrected counts conducted since 1991, our estimation method considered two major sources of error: spatial variation in distribution and Imperfect Detection. The Florida manatee is listed as endangered, but its status is currently under review; the present study may become important for the review process. Interestingly, we estimated that 70% (95%CI: 60–80%) of manatees on the east coast of Florida were aggregated in one county during our survey. Our study illustrates the value of combining information from multiple sources to monitor abundance at large scales. Integration of information can reduce cost, facilitate the use of data obtained from new technologies to increase accuracy, and contribute to encouraging coordination among survey teams from different organizations nationally or internationally. Finally, we discuss the applicability of our work to other conservation applications (e.g., risk assessment) and to other systems.
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an index of risk of co occurrence between marine mammals and watercraft example of the florida manatee
Biological Conservation, 2013Co-Authors: Sarah Bauduin, Stacie M Koslovsky, Holly H Edwards, Olivier Gimenez, Julien Martin, Daniel E FaganAbstract:Collisions between wildlife and vehicles represent a large source of mortality for many species. To implement effective protection zones, it is important to identify areas in which wildlife–vehicle collisions are likely to occur. We used statistical models to derive an index of risk of co-occurrence between manatees and boats. Our statistical models were used to predict the distribution of both manatees and boats, while accounting for observer-specific Detection probabilities. Models used aerial survey data and we found that both environmental and temporal covariates influenced manatee and boat distributions. Moreover, the probability of detecting manatees varied substantially with the weather and among observers. To our knowledge, this is the first time that manatee distribution is modeled as a function of key environmental and seasonal covariates, while accounting for Imperfect Detection of manatees. We computed an index of risk of co-occurrence by multiplying the probability of manatee occupancy by the expected boat density and occupancy to identify areas where manatee–boat collisions are likely to occur. This analytical framework emphasizes the importance of accounting for Imperfect Detection, and how modeling distribution of both organisms and vehicles as a function of key covariates can help improve predictions of risk of collisions. Risk of collision metrics can then be used in designing protection zones.
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an adaptive management framework for optimal control of hiking near golden eagle nests in denali national park
Conservation Biology, 2011Co-Authors: Julien Martin, James D Nichols, Paul L Fackler, Michael C Runge, Carol L Mcintyre, Bruce L Lubow, Maggie C Mccluskie, Joel A SchmutzAbstract:Unintended effects of recreational activities in protected areas are of growing concern. We used an adaptive-management framework to develop guidelines for optimally managing hiking activities to maintain desired levels of territory occupancy and reproductive success of Golden Eagles (Aquila chrysaetos) in Denali National Park (Alaska, U.S.A.). The management decision was to restrict human access (hikers) to particular nesting territories to reduce disturbance. The management objective was to minimize restrictions on hikers while maintaining reproductive performance of eagles above some specified level. We based our decision analysis on predictive models of site occupancy of eagles developed using a combination of expert opinion and data collected from 93 eagle territories over 20 years. The best predictive model showed that restricting human access to eagle territories had little effect on occupancy dynamics. However, when considering important sources of uncertainty in the models, including environmental stochasticity, Imperfect Detection of hares on which eagles prey, and model uncertainty, restricting access of territories to hikers improved eagle reproduction substantially. An adaptive management framework such as ours may help reduce uncertainty of the effects of hiking activities on Golden Eagles.
