Individual Capture Probabilities Research Articles

Applications of Underwater Video for Imperiled Fish Species Population Monitoring

Abstract Common freshwater fish sampling methods (e.g., seining and electrofishing) are inherently invasive and often not appropriate for imperiled species. Visual observation methods provide a noninvasive alternative for population monitoring. Despite increasing popularity, the use of underwater video to monitor imperiled fishes is relatively unexplored. We evaluated the efficacy of underwater video to estimate occurrence and abundance of the imperiled Comanche Springs Pupfish Cyprinodon elegans using both point observations and time intervals (surveys). We deployed camera traps at sites within major habitat types (pool, canal, and ciénaga) of Balmorhea State Park, Texas, United States in March and October 2019 (seasons). We detected Comanche Springs at all occupied sites in both seasons when viewing ~30 min of video. The species was detected at 80% of occupied sites when viewing ~10 min and ~5 min of video in March and October, respectively. Comanche Springs Pupfish detection probability was higher in October, with no variability among habitat types. On average, cumulative species detection probability was >0.9 with fifteen 60-s surveys. However, species detection probability of a single survey ranged from 0.02 to 0.62 (mean = 0.14). Although there was no variation between seasons or among habitats, variation was high in the detection of the maximum Comanche Springs Pupfish count among sites even with observations every 5 s. Individual capture probability from a repeated-count abundance model was less variable than species detection probability (0.01-0.33) and generally low (mean = 0.06). Site absolute abundance was generally comparable among major habitats, but with higher uncertainty with increasing maximum count. Our study provides a comprehensive assessment of underwater video for imperiled fish species population monitoring. The findings show a trade-off between processing effort and information loss and limitations associated with imperfect detection and individual capture common to any fish sampling method.

A spatial capture–recapture model with attractions between individuals

AbstractOver the past two decades there have been many advancements in modeling capture–recapture (CR) data to account for emerging data collection technology and techniques. Spatial capture–recapture (SCR) models have been introduced to estimate population size and numerous other demographic parameters from spatially explicit CR data. Recently SCR models have also begun incorporating realistic animal movement to account for individual dispersion and attraction to resources. While some species of animals are known to exhibit attractive behavior, nearly all SCR models assume complete independence among individual's movement and capture probability. In this article, we introduce an SCR model which allows for attractions between individuals via their daily location. We demonstrate via a simulation study that accounting for the attractions specified by our model, when present, can improve population size estimation. In addition, we apply our model to an iconic SCR dataset to estimate the population size and attraction parameters of a Bengal tiger (Panthera tigris tigris) population.

Laboratory experiments reveal effects of group size on hunting performance in yellow saddle goatfish, Parupeneus cyclostomus

Group hunting where two or more individuals chase the same prey occurs in various vertebrate clades. Its evolution has typically been attributed to the resulting increased prey capture rate. Indeed, many studies have documented positive correlations between the number of individuals and probability of success, suggesting that joint hunting is cooperative. However, all existing data on vertebrates are based on field observations and hence cannot control for factors that may also influence hunting success, such as variation in habitat structure, hunting motivation or prey state. Therefore, we conducted controlled laboratory experiments to test whether hunting performance is affected by group size in the group-hunting yellow saddle goatfish. We presented invertebrate prey in an artificial coral reef and measured hunting performance as a function of manipulated group size, ranging from one to four individuals. We found that individuals hunting alone failed to catch the prey within the trial time limit significantly more often than groups did. Furthermore, we found that time until capture depended on group size, with singletons being slowest and groups of four fastest. With few exceptions, group members were approximately equally likely to catch prey. Hunting success as a singleton did not predict an individual's prey capture probability when in a group, suggesting that group hunting involves some producer–scrounger dynamics. In conclusion, while the experiment cannot address the question of how group size affects individual net calorie intake, the results show that groups are more successful at hunting than singletons are.

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture-recapture data.

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles Myodes rufocanus from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.

A note on identifiability and maximum likelihood estimation for a heterogeneous capture-recapture model

This article discusses identifiability and maximum likelihood estimation for a closed population capture-recapture model with heterogeneity in capture probabilities. The model assumes that the individual capture probabilities arise from a discrete distribution over the interval Considering the complete likelihood, without applying any conditioning, we prove that identifiability holds under a restriction on the number of support points of the mixing distribution. Under this identifiability assumption, we present a simple closed-form iterative algorithm for maximum likelihood estimation. Interval estimation is carried by a bootstrap resampling procedure. The proposed methods are illustrated on a literature real data set and a simulation study is carried to assess the frequentist merits of different population size estimators.

Coping with intrasexual behavioral differences: Capture-recapture abundance estimation of male cheetah.

Population estimates are a fundamental requirement of ecology and conservation. While capture–recapture models are an established method for producing such estimates, their assumption of homogeneous capture probabilities is problematic given that heterogeneity in individual capture probability is inherent to most species. Such variation must be accounted for by abundance models; otherwise, biased estimates are risked.Here, we investigate the performance of four types of heterogeneity models for estimating abundance of male cheetah Acinonyx jubatus, a species with two distinct spatial tactics of territorial and nonterritorial (floater) males. The differences in spatial movements of territory holders and floaters are expected to result in intrasexual heterogeneous capture probabilities. Four heterogeneity models were used to model male abundance at five territories in central Namibia; (a) a spatial tactic model, (b) a finite mixture model, both run in program MARK, (c) a floater‐only model, and (d) a heterogeneity Mh model, both run in the program CAPTURE. Camera trap data of cheetah, taken at frequently visited marking trees, were used to derive true abundance. Model results were compared to the true abundance to assess the accuracy of estimates.Only models (a), (b), and (c) were able to consistently produce accurate results. Mixture models do not require prior knowledge regarding spatial tactic of males, which might not always be available. Therefore, we recommend such models as the preferred model type for cheetahs.Results highlight the potential for mixture models in overcoming the challenges of capture probability heterogeneity and in particular their use with species where intrasexual behavioral differences exist.

Indexing Salmonid Abundance in Small Streams Using Reduced Effort Electrofishing

A paradox in managing threatened and endangered species is the increased need for documenting population status, which in many instances requires capturing and handling individuals. Electrofishing is a widely used method for sampling fish in small streams, but the potential for detrimental effects call for its careful use. Methods that reduce exposure for individual fish, yet still provide useful abundance estimates, are therefore desirable. Using data from a juvenile steelhead (Oncorhynchus mykiss) monitoring program in Idaho, we quantified variation and bias in capture probability from three-pass depletion electrofishing, and evaluated a method for indexing abundance based on single-pass electrofishing. Capture probability varied primarily at the level of sampling visits, with little spatial variation (i.e. at the level of the study sites). Water temperature, channel depth and, to a lesser degree, discharge best described the variation in capture probability between sampling events. We found no effect of previous capture and handling on individual capture probability, and the capture probability did not differ between subyearling and overyearling fish. Finally, a simple mixed-effects model with study site as a random effect, which related first-pass catch to the associated multiple-pass removal estimate, explained 91% of the variation in our data. The main limitation of the approach is that it does not provide error propagation and confidence intervals to the abundance estimates. However, the approach can be useful where multiple depletion electrofishing data exist, and when a single point estimate is sufficient to monitor major population trends in small streams.

Applying the multistate capture–recapture robust design to characterize metapopulation structure

Summary Population structure must be considered when developing mark–recapture (MR) study designs as the sampling of individuals from multiple populations (or subpopulations) may increase heterogeneity in individual capture probability. Conversely, the use of an appropriate MR study design which accommodates heterogeneity associated with capture occasion varying covariates due to animals moving between ‘states’ (i.e. geographic sites) can provide insight into how animals are distributed in a particular environment and the status and connectivity of subpopulations. The multistate closed robust design (MSCRD) was chosen to investigate: (i) the demographic parameters of Indo‐Pacific bottlenose dolphin (Tursiops aduncus) subpopulations in coastal and estuarine waters of Perth, Western Australia; and (ii) how they are related to each other in a metapopulation. Using 4 years of year‐round photo‐identification surveys across three geographic sites, we accounted for heterogeneity of capture probability based on how individuals distributed themselves across geographic sites and characterized the status of subpopulations based on their abundance, survival and interconnection. MSCRD models highlighted high heterogeneity in capture probabilities and demographic parameters between sites. High capture probabilities, high survival and constant abundances described a subpopulation with high fidelity in an estuary. In contrast, low captures, permanent and temporary emigration and fluctuating abundances suggested transient use and low fidelity in an open coastline site. Estimates of transition probabilities also varied between sites, with estuarine dolphins visiting sheltered coastal embayments more regularly than coastal dolphins visited the estuary, highlighting some dynamics within the metapopulation. Synthesis and applications. To date, bottlenose dolphin studies using mark–recapture approach have focussed on investigating single subpopulations. Here, in a heterogeneous coastal–estuarine environment, we demonstrated that spatially structured bottlenose dolphin subpopulations contained distinct suites of individuals and differed in size, demographics and connectivity. Such insights into the dynamics of a metapopulation can assist in local‐scale species conservation. The MSCRD approach is applicable to species/populations consisting of recognizable individuals and is particularly useful for characterizing wildlife subpopulations that vary in their vulnerability to human activities, climate change or invasive species.

An integrated approach for addressing uncertainty in the delineation of groundwater management areas

Uncertainty is a pervasive but often poorly understood factor in the delineation of wellhead protection areas (WHPAs), which can discourage water managers and practitioners from relying on model results. To make uncertainty more understandable and thereby remove a barrier to the acceptance of models in the WHPA context, we present a simple approach for dealing with uncertainty. The approach considers two spatial scales for representing uncertainty: local and global. At the local scale, uncertainties are assumed to be due to heterogeneities, and a capture zone is expressed in terms of a capture probability plume. At the global scale, uncertainties are expressed through scenario analysis, using a limited number of physically realistic scenarios. The two scales are integrated by using the precautionary principle to merge the individual capture probability plumes corresponding to the different scenarios. The approach applies to both wellhead protection and the mitigation of contaminated aquifers, or in general, to groundwater management areas. An example relates to the WHPA for a supply well located in a complex glacial aquifer system in southwestern Ontario, where we focus on uncertainty due to the spatial distributions of recharge. While different recharge scenarios calibrate equally well to the same data, they result in different capture probability plumes. Using the precautionary approach, the different plumes are merged into two types of maps delineating groundwater management areas for either wellhead protection or aquifer mitigation. The study shows that calibrations may be non-unique, and that finding a "best" model on the basis of the calibration fit may not be possible.

Estimating abundance of mountain lions from unstructured spatial sampling

AbstractMountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km2) in west‐central Montana using 2 methods: snow back‐tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km2(95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km2for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

‘Exciting statistics’: the rapid development and promising future of hierarchical models for population ecology

Many biologists might indeed find statistics dull or even terrifying (van Emden, 2008), but ecologists and wildlife biologists should be downright ecstatic with recent developments in statistical modeling. One of the most exciting advances in recent decades is the development of hierarchical models. Hierarchical or multi-level models are becoming increasingly common in population analysis, and with good reason. Indeed, Kery & Schaub (2011) devote much of their final chapter to the ‘Power and Beauty of Hierarchical Models’, and among the many virtues they list for hierarchical models are simpler fitting of complex models, clarity of thinking about these models and a ‘step-up’ approach to model building (Kery & Schaub, 2011). Simpler and clearer model construction is an important advantage of hierarchical models, and although this may be reason enough to choose a hierarchical approach to modeling ecological data, the benefits do not stop with the fitting of the model to data. The property of shrinkage is a particularly important advantage of hierarchical models in ecology (Gelman et al., 2004; Gelman & Hill, 2006; Ntzoufras, 2011; Cam, 2012; Schaub & Kery, 2012). Shrinkage (or ‘borrowing strength’) refers to the assumption of random effects to be realizations of a process governed by a statistical distribution (Gelman et al., 2004; Cam, 2012; Schaub & Kery, 2012). This stochastic assumption means that realizations of the process (such as daily risk of mortality at sites, in the present case) that are poorly estimated because of small sample size, short study duration, etc., but that fall far from the population mean will be pulled toward the mean; those realizations that are strongly supported by data contribute more information about the distribution from which the stochastic realizations are drawn, and are pulled less strongly toward the mean (Gelman et al., 2004; Gelman & Hill, 2006). Including random effects in models therefore allows the estimation of parameters for realizations with small sample sizes, guards against spurious conclusions, and increases the precision of parameter estimates for all realizations of the process of interest (Gelman & Hill, 2006; Cam, 2012; Schaub & Kery, 2012). Another important benefit of hierarchical models is reduction in bias. Where unmeasured (or unmeasurable) variables affect a parameter of interest, estimates of that parameter can be biased; applying a random effect allows for such unobserved heterogeneity and reduces the bias associated with unmeasured covariates (Gelman & Hill, 2006; Cam, 2012). The many advantages of hierarchical models for animal conservation apply not only to shared frailty models (Heisey et al., 2010; Griffin et al., 2011; Halstead et al., 2012), but also to modeling individual frailties in survival and reproduction (Cam et al., 2002; Wintrebert et al., 2005), accounting for heterogeneity in individual capture probabilities (Dorazio & Royle, 2003), discerning patterns among demographic parameters (Link & Barker, 2005) and combining inference for mark-recapture models (Rivot & Prevost, 2002; Calvert et al., 2009; Papadatou et al., 2010, 2012) among many, many other applications. Although all commentators correctly acknowledge that algebraically identical hierarchical models can be analyzed under a frequentist or Bayesian approach, the latter offers the ecologist several important advantages. Modern software allows the simple implementation of complex models that would be difficult to analyze in a likelihood framework (Royle & Dorazio, 2008; Kery & Schaub, 2011; Schaub & Kery, 2012). This frees ecologists to focus on developing interesting, relevant models for their systems rather than writing complex likelihood functions (Kery, 2010; Kery & Schaub, 2011; Schaub & Kery, 2012). The consistent treatment of fixed effects, random effects, and missing data is another advantage of the Bayesian paradigm (Link & Barker, 2010; Heisey, 2012). Calculation of derived parameters and out-of-sample predictions, and propagating error associated with these quantities, is simple and does not rely upon asymptotic approximations (Kery, 2010; Link & Barker, 2010); such derived parameters and predictions are commonly of interest in population models. Updating prior bs_bs_banner

Transit station or destination? Attendance patterns, movements and abundance estimate of humpback whales off west South Africa from photographic and genotypic matching

Humpback whales Megaptera novaeangliae found off west South Africa (WSA) are known to display an atypical migration that may include temporary residency and feeding during spring and summer. At a regional scale there is uncertainty about how these whales relate to the greater West African Breeding Stock B as a whole, with evidence both for and against its division into two substocks. A database containing sighting information of humpback whales intercepted by boat in the WSA region from 1983 to 2008 was compiled. It included a total of 1 820 identification images of ventral tail flukes and lateral views of dorsal fins. After systematic within- and between-year matching of images of usable quality, it yielded 154 different individuals identified by tail flukes (TF), 230 by left dorsal fins (LDF), and 237 by right dorsal fins (RDF). Microsatellite (MS) matching of 216 skin biopsies yielded 156 individuals. By linking all possible sightings of the same individuals using all available identification features, the periodicity and seasonality of 281 individual whales were examined. In all, 60 whales were resighted on different days of which 44 were between different calendar years. The most resightings for one individual was 11 times, seen in six different years, and the longest interval between first and last sightings was about 18 years. A resighting rate of 15.6% of whales at intervals of a year or more indicates long-term fidelity to the region. Shorter intervals of 1–6 months between sequential sightings in the same year may suggest temporary residency. The TF image collection from WSA was compared to TF collections from four other regions, namely Gabon, Cabinda (Angola), Namibia and the Antarctic Humpback Whale Catalogue (AHWC). Three matches were detected between WSA (in late spring or summer) and Gabon (in winter), confirming direct movement between these regions. The capture–recapture data of four different identification features (TF, RDF, LDF and MS) from six successive subsets of data from periods with the highest collection effort (2001–2007) were used to calculate the number of whales that utilise the region, using both closed- and open-population models. Dorsal fins have never been used to estimate abundance for humpback whales, so the different identification features were evaluated for potential biases. This revealed 9–14% incidence of missed matches (false negatives) when using dorsal fins that would result in an overestimate, whereas variation in individual fluke-up behaviour may lower estimates by as much as 57–66% due to heterogeneity of individual capture probability. Taking into consideration the small dataset and low number of recaptures, the most consistent and precise results were obtained from a fully time-dependent version of the Jolly-Seber open-population model, with annual survival fixed at 0.96, using the MS dataset. This suggests that the WSA feeding assemblage during the months of spring and summer (September–March) of the study period numbered about 500 animals. The relationship of these whales to those (perhaps strictly migratory) that may occur in other seasons of the year, and their links to possible migratory routes and other feeding or breeding areas, remain uncertain.

A new method for estimating animal abundance with two sources of data in capture–recapture studies

Summary 1. Mark–recapture studies are often used to estimate population size based on a single source of individual identification data such as natural markings or artificial tags. However, with the development of molecular ecology, multiple sources of identification can be obtained for some species and combining them to obtain population size estimates would certainly provide better information about abundance than each survey can provide alone. 2. We propose an extension of the Jolly–Seber model to infer abundance by combining two sources of capture–recapture data. The need to merge both sources of data was motivated by studies of humpback whales in which both photo‐identification and DNA from skin biopsy samples are often collected. As whales are not necessarily available by both sampling methods on any given occasion, they can appear twice in the combined data set if no combined sampling ever occurred during the survey, i.e. being photographed and genotyped on the same occasion. Our model thus combines the two sources of information by estimating the possible overlap. Monte Carlo simulations are used to assess the properties of the present estimator that is then used to estimate the size of the humpback whale population in New Caledonia. The new open‐population estimator is also compared with classic closed‐population estimators incorporating either temporal and/or individual heterogeneity in the capture probability: the purpose was to evaluate which approach (closed or open population) was the least biased for an open population with individual heterogeneous capture probabilities. 3. When all assumptions are met, the estimator is unbiased as long as the probability of being double‐tagged (e.g. photographed and biopsied on the same occasion) on every occasion is above 0·2. 4. The humpback whale case study in New Caledonia shows that our two‐source Jolly–Seber (TSJS) estimator could be more efficient in estimating population size than models based only on one type of data. For monitoring purposes, the proposed method provides an efficient alternative to the existing approaches and a productive direction for future work to deal with multiple sources of data to estimate abundance. 5. R‐codes formatting the data and implementing the TSJS model are provided in Resource S5.

Addressing challenges in non invasive capture-recapture based estimates of small populations: a pilot study on the Apennine brown bear

It is often difficult to determine optimal sampling design for non-invasive genetic sampling, especially when dealing with rare or elusive species depleted of genetic diversity. To address this problem, we ran a hair-snag pilot study on the remnant Apennine brown bear population. We used occupancy models to estimate the performance of an improved field protocol, a meta-analysis approach to indirectly model capture probability, and simulations to evaluate the effect of genotyping errors on the accuracy of capture-recapture population estimates. In spring 2007 we collected 70 bear hair samples in 15 5 × 5 km cells, using 5 10-day trapping sessions. Bear detectability was higher in 2007 than in a previous attempt on the same population in 2004, reflecting improved field protocols and sampling design. However, individual capture probability was 0.136 (95% CI = 0.120–0.152), still below the minimum requirements of capture-mark-recapture closed population models. We genotyped hair samples (n = 63) at 9 microsatellite loci, obtaining 94% Polymerase Chain Reaction success, and 13 bear genotypes. Estimated PIDsib was 0.00594, and per-genotype error rate was 0.13, corresponding to a 99% probability of correct individual identification. Simulation studies showed that the effect of non-corrected or filtered genetic errors on the accuracy of population estimates was negligible only when individual capture probability was >0.2. Our results underline how the interaction among field protocols, sampling strategies and genotyping errors may affect the accuracy of DNA-based estimates of small and genetically depleted populations, and warned us about the feasibility of a survey using only traditional hair-snag sampling. In this and similar cases, indications from pilot studies can provide cost-effective means to evaluate the efficiency of designed sampling and modelling procedures.

On the Nonidentifiability of Population Sizes

When a nonparametric mixture model is adopted to deal with the heterogeneity among individual capture probabilities, the population size is nonidentifiable (Link, 2003, Biometrics 59, 1123-1130). Holzmann, Munk, and Zucchini (2006, Biometrics 62, 934-936) discussed the conditions under which a subfamily of mixing distributions is identifiable. Link (2006, Biometrics 92, 936-939) found that the nonidentifiability occurs across identifiable subfamilies. It is shown that there is a subfamily in which each mixing distribution is determined by its mixture, and the population size admits estimable lower bounds that can be used to construct lower confidence limits.

Why a time effect often has a limited impact on capture‐recapture estimates in closed populations

AbstractThe author is concerned with log‐linear estimators of the size N of a population in a capture‐recapture experiment featuring heterogeneity in the individual capture probabilities and a time effect. He also considers models where the first capture influences the probability of subsequent captures. He derives several results from a new inequality associated with a dispersive ordering for discrete random variables. He shows that in a log‐linear model with inter‐individual heterogeneity, the estimator N is an increasing function of the heterogeneity parameter. He also shows that the inclusion of a time effect in the capture probabilities decreases N in models without heterogeneity. He further argues that a model featuring heterogeneity can accommodate a time effect through a small change in the heterogeneity parameter. He demonstrates these results using an inequality for the estimators of the heterogeneity parameters and illustrates them in a Monte Carlo experiment

Ecological differentiation between the Sardinian endemic Maniola nurag and the pan-European M. jurtina

Recently refined evolutionary theories have highlighted that ecological interactions and environmental gradients can play a major role in speciation. This paper reports on a 3-year field study, in which the ecology of two congeneric butterfly species was used to explore and compare the environmental factors determining their spatial distribution. These data are discussed in the context of possible speciation scenarios between the Sardinian populations of Maniola nurag and M. jurtina. M. nurag is endemic to the island of Sardinia, while M. jurtina is widespread over Europe. In Sardinia, the two species are locally sympatric. Mark-release-recapture experiments were combined with measures of environmental variables in 15 1-ha plots, established in areas of potential habitat for the butterflies. Constrained linear models were parameterized from mark-recapture data to estimate both individual (survival and capture probabilities) and population (population size and recruitment) parameters. The two species had similar demography, movement patterns, life history, and behaviour. Population sizes developed in a parabolic fashion from beginning to end of the flight season. Differences included local population size, adult phenology, and habitat requirements. Long-distance movements larger than 1.5 km were observed, suggesting a substantial amount of gene-flow between populations of the endemic as well as the widespread species. Multivariate analyses revealed four main environmental gradients responsible for the abundance of the butterflies in an area. Both species responded similarly to environmental variables. However, each species's abundance was correlated with a different environmental gradient determined by vegetation cover and structure. When sympatric, the two species responded to subtle differences in microhabitat structure. This might originally have induced their divergence. This study is an example of how empirical field data on population dynamics, dispersal, and habitat characteristics of two sympatric congeners can further our understanding of how species differentiate despite existing gene-flow. (c) 2006 The Linnean Society of London, Biological Journal of the Linnean Society, 2006, 89, 561-574.

Estimating population sizes for capture–recapture sampling with binomial mixtures

A binomial mixture model has been used to account for heterogeneous individual capture probabilities in capture–recapture studies. The problem of estimating the population size is reduced to estimating the odds that an individual is not captured. The odds is nonidentifiable. There is no upper bound to the odds. The odds admits lower bounds that can yield estimable lower bounds to the population size. An example is studied. A simulation experiment is used to evaluate the proposed and existing estimators.

Density estimation in live‐trapping studies

Unbiased estimation of population density is a major and unsolved problem in animal trapping studies. This paper describes a new and general method for estimating density from closed‐population capture–recapture data. Many estimators exist for the size (N) and mean capture probability ( p̄) of a closed population. These statistics suffer from an unknown bias due to edge effect that varies with trap layout and home range size. The mean distance between successive captures of an individual () provides information on the scale of individual movements, but is itself a function of trap spacing and grid size. Our aim is to define and estimate parameters that do not depend on the trap layout. In the new method, simulation and inverse prediction are used to estimate jointly the population density (D) and two parameters of individual capture probability, magnitude (g0) and spatial scale (σ), from the information in , p̄ and . The method uses any configuration of traps (e.g. grid, web or line) and any choice of closed‐population estimator. It is assumed that home ranges have a stationary distribution in two dimensions, and that capture events may be simulated as the outcome of competing Poisson processes in time. The method is applied to simulated and field data. The estimator appears unusually robust and free from bias.

Theory & Methods: A parametric empirical Bayes approach to the analysis of capture‐recapture experiments

Empirical Bayes methods and a bootstrap bias adjustment procedure are used to estimate the size of a closed population when the individual capture probabilities are independently and identically distributed with a Beta distribution. The method is examined in simulations and applied to several well‐known datasets. The simulations show the estimator performs as well as several other proposed parametric and non‐parametric estimators.