Explore the vast range of titles available.

1. India's Chambal River hosts the largest population of the critically endangered gharial. Boat-based daylight surveys to date only provide indices of relative abundance, without measures of survey bias or error. No attempt to quantify detection probabilities in these surveys has yet been made, and thus, absolute density estimates of this population remain unknown. 2. We surveyed 75 km of the River Chambal and photographed individual gharials for capture—recapture analysis. The total sampling effort yielded 400 captures. Population closure was supported (z = -1·48, P = 0·069), and closed-population models were used to estimate abundances. 3. Models were selected using the Akaike Information Criterion (AIC) index of model fit. The best model estimated 231 ± 32 adult, 83 ± 23 subadult and 89 ± 19 juvenile gharials (Mean ± SE), respectively, while the model-averaged estimate was 220 ± 28 adult, 76 ± 16 subadults and 93 ± 16 juvenile gharials, respectively. 4. The best model estimated absolute densities of 3·08 ± 0·43, 1·11 ± 0·3 and 1·19 ± 0·25 adult, subadult and juvenile gharials km -1 , respectively, while the model-averaged estimate was 2·93 ± 0·37, 1·01 ± 0·21 and 1·24 ± 0·21 adult, subadult and juvenile gharials km -1 , respectively, compared with relative densities of 0·94, 0·45 and 0·30 adult, subadult and juvenile gharials km -1 , respectively, from boat-based daylight surveys. On the basis of our best model, we suggest a detection probability based correction factor of 3·27, 2·47 and 3·97 to boat-based daylight survey estimates of adult, subadult and juvenile gharials, respectively. 5. Synthesis and applications. Used within the framework of capture—recapture analysis, photoidentification provides a reliable and noninvasive method of estimating population size and structure in crocodilians. We also opine that without determining the current status of gharials, highly intensive strategies, such as the egg-collection and rear-and-release programmes being implemented currently, initiated on the basis of underestimates of population sizes, are unwarranted and divert valuable conservation resources away from field-based protection measures, which are essential in the face of threats like hydrologic diversions, sand mining, fishing and bankside cultivation.

Densities of elusive terrestrial mammals are commonly estimated from camera-trap data. Typically, this is a 2-step process involving 1) fitting conventional closed population capture-recapture models to estimate abundance, and 2) using ad hoc methods to determine the effective trapping area. The methodology needs to be accurate, robust, and reliable when results are used to guide wildlife management. We critically review 47 published studies and discuss the problems associated with contemporary population estimates of elusive species from camera-trap data. In particular we discuss 1) individual identification, 2) sample size and capture probability, 3) camera location and spacing, 4) the size of the study area, and 5) ad hoc density estimation from the calculation of an effective trapping area. We also discuss the recently developed spatially explicit capture-recapture (SECR) models as an alternative approach that does not require the intermediate step of estimating an effective trapping area. We recommend 1) greater transparency in study design and quality of the data, 2) greater rigor when reviewing manuscripts, and 3) that more attention is given to the survey design to ensure data are of sufficient quality for analysis.

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. We assessed the importance of including capture history information in the estimation of density dependence from capture–recapture data. We performed an extensive simulation study, evaluating the performance of different methods in the estimation of different population processes. We found that disregarding sampling error can bias the density‐dependence estimates in low variance autoregressive population models.

Misidentification of animals is potentially important when naturally existing features (natural tags) are used to identify individual animals in a capture–recapture study. Photographic identification (photoID) typically uses photographic images of animals' naturally existing features as tags (photographic tags) and is subject to two main causes of identification errors: those related to quality of photographs (non‐evolving natural tags) and those related to changes in natural marks (evolving natural tags). The conventional methods for analysis of capture–recapture data do not account for identification errors, and to do so requires a detailed understanding of the misidentification mechanism. Focusing on the situation where errors are due to evolving natural tags, we propose a misidentification mechanism and outline a framework for modeling the effect of misidentification in closed population studies. We introduce methods for estimating population size based on this model. Using a simulation study, we show that conventional estimators can seriously overestimate population size when errors due to misidentification are ignored, and that, in comparison, our new estimators have better properties except in cases with low capture probabilities (<0.2) or low misidentification rates (<2.5%).

For bumble bees and other social organisms, colonies are the functional unit of the population rather than the individual workers. Estimates of bumble bee nest density are thus critical for understanding population distribution and trends of this important pollinator group. Yet, surveys of bumble bee nests and other taxa with sessile life stages rarely account for imperfect detection. Here, we demonstrate the use of mark-recapture methods to estimate the density of bumble bee nests at multiple sites using standardized survey protocols. We detected ~ 30% of nests in a 2-h survey of each 3000 m2 plot. We determined that 4–5 visits were sufficient to estimate the total number of nests at our site with reasonable precision, equating to one-third the effort previously assumed necessary to reliably estimate nest density. Mark-recapture approaches can be used to generate unbiased estimates of density with reduced search effort, while simultaneously increasing the rate at which nests are discovered.

An infectious disease can cause a detrimental effect on national security. A group such as the military called a “closed population”, which is a subset of the general population but has many distinct characteristics, must survive even in the event of a pandemic. Hence, it requires its own distinct solution during a pandemic. In this study, we investigate a simulation analysis for implementing an agent-based model that reflects the characteristics of agents and the environment in a closed population and finds effective control measures for making the closed population functional in the course of disease spreading.

Animal identification based on DNA samples and microsatellite genotypes is widely used for capture-recapture studies. The method shows promise in field protocols and potentially minimal error rates in the DNA analysis. Some studies show much higher error rates in individual identification. There will be some level of uncertainty, although in some situations the uncertainty level is small, in the identification of individuals from microsatellite genotypes.

Estimates of roosting habitat availability and population size using unbiased sampling regimes are completely lacking for any bat species. The use of conspicuous and accessible roosts in the developing, rolled leaves of Heliconia and Calathea plants by Thyroptera tricolor (Spix's disc-winged bat) provided an ideal opportunity to address this need. To assess roost availability and population size, the number of occupied and unoccupied leaves and bats in known areas in an area of lowland rain forest in north-eastern Costa Rica were quantified in 1998–99. A high density of leaves was available on any given day (mean: 43 leaves ha−1), but the density of roost leaves was low (mean: 2.5 leaves ha−1), corresponding with a low occupancy rate of 5.7 or 12% based on different methods of estimation. Developing leaves were available for 8–16 h in the preferred size range of leaves used by T. tricolor, and a maximum of 28–60 h, depending on the plant species. Using closed-population mark–recapture models, the 5.69-ha study area supported 261 individuals over a 4-mo period in 1998, corresponding to a density of 43 bats ha−1. These results have important implications for the results of studies on bat community structure and rarity, and for the behaviour and ecology of T. tricolor.

1 Most plant demographic studies follow marked individuals in permanent plots. Plots tend to be small, so detectability is assumed to be one for every individual. However, detectability could be affected by factors such as plant traits, time, space, observer, previous detection, biotic interactions, and especially by life-state. 2 We used a double-observer survey and closed population capture-recapture modelling to estimate state-specific detectability of the orchid Cleistes bifaria in a long-term study plot of 41.2 m2. Based on$\\text{AIC}_{\\text{c}}$model selection, detectability was different for each life-state and for tagged vs. previously untagged plants. There were no differences in detectability between the two observers. 3 Detectability estimates (SE) for one-leaf vegetative, two-leaf vegetative, and flowering/fruiting states correlated with mean size of these states and were 0.76 (0.05), 0.92 (0.06), and 1 (0.00), respectively, for previously tagged plants, and 0.84 (0.08), 0.75 (0.22), and 0 (0.00), respectively, for previously untagged plants. (We had insufficient data to obtain a satisfactory estimate of previously untagged flowering plants). 4 Our estimates are for a medium-sized plant in a small and intensively surveyed plot. It is possible that detectability is even lower for larger plots and smaller plants or smaller life-states (e.g. seedlings) and that detectabilities < 1 are widespread in plant demographic studies. 5 State-dependent detectabilities are especially worrying since they will lead to a size- or state-biased sample from the study plot. Failure to incorporate detectability into demographic estimation methods introduces a bias into most estimates of population parameters such as fecundity, recruitment, mortality, and transition rates between life-states. We illustrate this by a simple example using a matrix model, where a hypothetical population was stable but, due to imperfect detection, wrongly projected to be declining at a rate of 8% per year. 6 Almost all plant demographic studies are based on models for discrete states. State and size are important predictors both for demographic rates and detectability. We suggest that even in studies based on small plots, state- or size-specific detectability should be estimated at least at some point to avoid biased inference about the dynamics of the population sampled.

Closed population capture-recapture analysis of camera-trap data has become the conventional method for estimating the abundance of individually recognisable cryptic species living at low densities, such as large felids. Often these estimates are the only information available to guide wildlife managers and conservation policy. Capture probability of the target species using camera traps is commonly heterogeneous and low. Published studies often report overall capture probabilities as low as 0.03 and fail to report on the level of heterogeneity in capture probability. We used simulations to study the effects of low and heterogeneous capture probability on the reliability of abundance estimates using the Mh jack-knife estimator within a closed-population capture-recapture framework. High heterogeneity in capture probability was associated with under- and over-estimates of true abundance. The use of biased abundance estimates could have serious conservation management consequences. We recommend that studies present capture frequencies of all sampled individuals so that policy makers can assess the reliability of the abundance estimates.