Catch composition, catch per unit effort (CPUE) and species selectivity of fishing gears on multi-species Kaptai Lake in Bangladesh

Kaptai Lake, the largest artificial reservoir in Southeast Asia, is home to a diverse fish fauna that supports thousands of livelihoods and is distinguished by multi-species and multi-gear fisheries. In Kaptai Lake, the gear-based catch composition, catch rate and distribution pattern are little known. From August 2020 to April 2021, a nine-month study was conducted in five upazilas using direct catch assessment surveys and fishing effort surveys from four fishing gears, namely seine nets, gill nets, lift nets, and push nets. A total of 49 morpho-species from 22 families were found, with three species from the Clupeidae accounting for 93.63 % of the catch in all gear combined. The total catch composition and CPUE were higher in seine nets (75.07 %, 13.86 ± 1.8 kg/gear/trip respectively) and lower in lift nets (4.97 %, 1.01 ± 0.21 kg/gear/trip) and showed significant differences among gears, except sampling sites whereas CPUE was higher in Naniarchar for seine nets (17.29 ± 8.89 kg/gear/trip) and lower in Langadu for lift nets (0.62 ± 0.25 kg/gear/trip). Seine nets captured more species, and the number of species increased significantly as CPUE increased. Our study assessed four gears that targeted different fish species with little overlap in leading species; seine nets and gill nets primarily targeted Clupeidae (96.53 % and 41.69 %, respectively), whereas lift nets and push nets primarily targeted Cyprinidae and Palaemonidae (38.93 % and 99.37 % respectively). The observed abundance and variety of fish species captured in gill nets suggest a significant overlap in the selectivity of this fishing method with that of lift nets. Due to the varying contributions of sites and gears, the nMDS ordination pattern reveals a weak spatial variation in catch composition. According to the SIMPER results, Bagridae, Gobiidae, and Ambassidae were the most significant contributors to site grouping patterns across all gears. Furthermore, the findings indicate that the catch composition does not follow the typical pattern of spatial variation. By implementing measures to eliminate or decrease the usage of small mesh nets, there is expected to be a corresponding decrease in the capture of small fish. Additionally, this action will help mitigate the issue of overlapping selectivity among the current fishing gears. Our findings provide baseline data on the potential efficacy of gear limitation and suggest a gear-based management strategy.

Citizen Science Data Collection for Integrated Wildlife Population Analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

A COMPARISON OF MULTINOMIAL LIKELIHOOD AND CHI-SQUARE APPROACHES TO STATISTICAL POPULATION RECONSTRUCTION

Statistical population reconstruction using age-at-harvest and catch-effort data has recently emerged as a robust and versatile approach to estimating the demographic dynamics of harvested populations of wildlife. Although most reconstruction efforts employ the multinomial likelihood approach to identify which set of model parameters best describes the observed age-at-harvest and catch-effort data, using a [Formula: see text] objective function may provide a suitable alternative with a less steep learning curve. Using a harvested population of North American river otter (Lontra canadensis) in Kentucky as a case study, we investigated the performance of population reconstruction using multinomial likelihood and chi-square formulations. We simulated populations under a range of conditions and found that both the accuracy and precision of reconstruction estimates were similar under the two approaches. These results illustrate the potential benefits of using the [Formula: see text] approach, which may also allow agencies to incorporate auxiliary information from studies for which the corresponding likelihood contributions have yet to be developed.

Relationships of catch-per-unit-effort metrics with abundance vary depending on sampling method and population trajectory

Catch-per-unit-effort (CPUE) is often used to monitor wildlife populations and to develop statistical population models. Animals caught and released are often not included in CPUE metrics and their inclusion may create more accurate indices of abundance. We used 21 years of detailed harvest records for bobcat (Lynx rufus) in Wisconsin, U.S.A., to calculate CPUE and ‘actual CPUE’ (ACPUE; including animals caught and released) from bobcat hunters and trappers. We calibrated these metrics to an independent estimate of bobcat abundance and attempted to create simple but effective models to estimate CPUE and ACPUE using harvest success data (i.e., bobcats harvested/available permits). CPUE showed virtually no relationship with bobcat abundance across all years, but both CPUE and ACPUE had stronger, non-linear, and negative relationships with abundance during the periods when the population was decreasing. Annual harvest success strongly predicted composite ACPUE and CPUE from hunters and trappers and hunter ACPUE and CPUE but was a poorer predictor of trapper ACPUE and CPUE. The non-linear, and sometimes weak, relationships with bobcat abundance likely reflect the increasing selectivity of bobcat hunters for trophy animals. Studies calibrating per-unit-effort metrics against abundance should account for population trajectories and different harvest methods (e.g., hunting and trapping). Our results also highlight the potential for estimating per-unit-effort metrics from relatively simple and inexpensive data sources and we encourage additional research into the use of per-unit-effort metrics for population estimation.

Estimating disease survey intensity and wildlife population size from the density of survey devices: Leg-hold traps and the brushtail possum

Wildlife disease surveillance requires accurate information on the proportion of managed populations sampled or their population density, parameters that are typically expensive to measure. However, these parameters can be estimated using spatially explicit modelling of capture probabilities, based on the distribution and deployment times of capture devices, given accurate information on the relationships between these variables. This approach is used in New Zealand's surveillance programme aimed at confirming areas free of bovine tuberculosis (bTB¹) in brushtail possums (Trichosurus vulpecula). However, there is uncertainty about the accuracy of the underpinning parameters characterizing possum trappability (g), given the distance between where a trap is placed and the possum home range centre. Sampling intensity (SI: the percentage of the population sampled during a population survey) and sigma (σ; 95% home range radius/2.45) were measured, using leg-hold traps deployed under a set protocol to standardize survey effort, at four sites containing previously radio- and GPS-collared individuals. Those data were used to derive an estimate of the nightly probability of capture of possums in a trap set at their home range centre (g₀). Those estimates were compared to the standard assumptions currently used as defaults in the day-to-day approach used by bTB managers. Home-range size (and therefore σ) varied widely between sites (range 3.6-49.4 ha), probably largely in response to differences in possum density. Field measured SI also varied widely between sites, and was closely positively correlated with home range size (R² = 0.967; P =  0.017); wide-ranging possums were more trappable than sedentary ones. We found that g₀ was inversely related to σ, but the magnitude of increases in g₀ with declining σ appeared to be insufficient to compensate for the fewer places at which each possum could be trapped when those home ranges were small. SI was, therefore, not constant across sites where a standard survey effort was applied. The assumed relationship between g₀ and σ in the current spatial model may, therefore, need reassessment. The management implication of these result is that the sampling effort required to attain a target sampling intensity is dependant on the target animal density, and for bTB management of possums in New Zealand, is under-estimated by the current default parameters in a model of freedom-from-disease for higher density possum populations.

Costs and effectiveness of damage management of an overabundant species (Sus scrofa) using aerial gunning

Context Management of overabundant or invasive species is a constant challenge because resources for management are always limited and relationships between management costs, population density and damage costs are complex and difficult to predict. Metrics of management success are often based on simple measures, such as counts, which may not be indicative of impacts on damage reduction or cost-effectiveness under different management plans. Aims The aims of this study were to evaluate the effectiveness of aerial gunning for the management of wild pigs (Sus scrofa), and to evaluate how cost-effectiveness would vary under different relationships between levels of damage and densities of wild pigs. Methods Repeated reduction events were conducted by aerial gunning on three consecutive days at three study sites. Using a removal model, the proportion of the population removed by each flight was estimated and population modelling was used to show the time it would take for a population to recover. Three possible damage–density relationships were then used to show the level of damage reduction (metric of success) from different management intensities and levels of population recovery, and these relationships were expressed in terms of total costs (including both damage and management costs). Key results Populations were typically reduced by ~31% for the first flight, ~56% after two flights and ~67% after three flights. When the damage relationship suggests high damage even at low densities, the impact of one, two or three flights would represent a reduction in damage of 2%, 19% and 60% respectively after 1 year. Different damage relationships may show considerable damage reduction after only one flight. Removal rates varied by habitat (0.05 per hour in open habitats compared with 0.03 in shrubby habitats) and gunning team (0.03 versus 0.05). Conclusions Monitoring the efficacy of management provides critical guidance and justification for control activities. The efficacy of different management strategies is dependent on the damage–density relationship and needs further study for effective evaluation of damage reduction efforts. Implications It is critically important to concurrently monitor density and damage impacts to justify resource needs and facilitate planning to achieve a desired damage reduction goal.

Assessment of river otter abundance following reintroduction

Context By the early 1900s, river otters (Lontra canadensis) were extirpated across large parts of their range in North America. Over the last several decades they have made a remarkable recovery through widespread reintroduction programs. River otters were reintroduced in Ohio, USA, between 1988 and 1993, and restricted and limited harvesting of this population began in 2005. While circumstantial evidence points to rapid population growth following the reintroduction, changes in population size over time is unknown. Aims We sought to model river otter population growth following reintroduction, and to assess the impact of harvesting. Methods We used empirical and literature-based data on river otter demographics in Ohio to estimate abundance from 1988–2008 using an age- and sex-specific stochastic Leslie matrix model. Additionally, we used statistical population reconstruction (SPR) methods to estimate population abundance of river otters in Ohio from 2006 to 2008. Results Our Leslie matrix model predicted a population size of 4115 (s.d. = 1169) in 2005, with a population growth rate (λ) of 1.28 in 2005. Using SPR methods we found that both trapper effort and initial population abundance influenced our population estimates from 2006 to 2008. If we assumed that river otter pelt price was an accurate index of trapper effort, and if the initial population was between 2000 and 4000, then we estimated the λ to be 1.27–1.31 in 2008 and the exponential rate to be 0.17–0.21 from 2006 to 2008. Conversely, if the river otter population in 2005 was 1000, then we estimated λ to be 1.20 in 2008 and the exponential rate to be 0.08 from 2006 to 2008. Conclusions The river otter population in Ohio appears to have had the potential to grow rapidly following reintroduction. The ultimate effect of the harvesting regime on population abundance, however, remains clouded by limited data availability and high variability. Implications The considerable uncertainty surrounding population estimates of river otters in Ohio under the harvesting regime was largely driven by lack of additional data. This uncertainty clouds our understanding of the status of river otters in Ohio, but a more robust, long-term monitoring effort would provide the data necessary to more precisely monitor the population.

Random effects models and multistage estimation procedures for statistical population reconstruction of small game populations

Recently, statistical population models using age-at-harvest data have seen increasing use for monitoring of harvested wildlife populations. Even more recently, detailed evaluation of model performance for long-lived, large game animals indicated that the use of random effects to incorporate unmeasured environmental variation, as well as second-stage Horvitz-Thompson-type estimators of abundance, provided more reliable estimates of total abundance than previous models. We adapt this new modeling framework to small game, age-at-harvest models with only young-of-the-year and adult age classes. Our Monte Carlo simulation results indicate superior model performance for the new modeling framework, evidenced by lower bias and proper confidence interval coverage. We apply this method to male wild turkey harvest in the East Ozarks turkey productivity region, Missouri, USA, where statistical population reconstruction indicates a relatively stationary population for 1996-2010.

Comparison of statistical population reconstruction using full and pooled adult age-class data

Background

Age-at-harvest data are among the most commonly collected, yet neglected, demographic data gathered by wildlife agencies. Statistical population construction techniques can use this information to estimate the abundance of wild populations over wide geographic areas and concurrently estimate recruitment, harvest, and natural survival rates. Although current reconstruction techniques use full age-class data (0.5, 1.5, 2.5, 3.5, … years), it is not always possible to determine an animal's age due to inaccuracy of the methods, expense, and logistics of sample collection. The ability to inventory wild populations would be greatly expanded if pooled adult age-class data (e.g., 0.5, 1.5, 2.5+ years) could be successfully used in statistical population reconstruction.

Methodology/Principal Findings

We investigated the performance of statistical population reconstruction models developed to analyze full age-class and pooled adult age-class data. We performed Monte Carlo simulations using a stochastic version of a Leslie matrix model, which generated data over a wide range of abundance levels, harvest rates, and natural survival probabilities, representing medium-to-big game species. Results of full age-class and pooled adult age-class population reconstructions were compared for accuracy and precision. No discernible difference in accuracy was detected, but precision was slightly reduced when using the pooled adult age-class reconstruction. On average, the coefficient of variation increased by 0.059 when the adult age-class data were pooled prior to analyses. The analyses and maximum likelihood model for pooled adult age-class reconstruction are illustrated for a black-tailed deer (Odocoileus hemionus) population in Washington State.

Conclusions/Significance

Inventorying wild populations is one of the greatest challenges of wildlife agencies. These new statistical population reconstruction models should expand the demographic capabilities of wildlife agencies that have already collected pooled adult age-class data or are seeking a cost-effective method for monitoring the status and trends of our wild resources.

Integrated population modeling of black bears in Minnesota: implications for monitoring and management

Background

Wildlife populations are difficult to monitor directly because of costs and logistical challenges associated with collecting informative abundance data from live animals. By contrast, data on harvested individuals (e.g., age and sex) are often readily available. Increasingly, integrated population models are used for natural resource management because they synthesize various relevant data into a single analysis.

Methodology/Principal Findings

We investigated the performance of integrated population models applied to black bears (Ursus americanus) in Minnesota, USA. Models were constructed using sex-specific age-at-harvest matrices (1980–2008), data on hunting effort and natural food supplies (which affects hunting success), and statewide mark–recapture estimates of abundance (1991, 1997, 2002). We compared this approach to Downing reconstruction, a commonly used population monitoring method that utilizes only age-at-harvest data. We first conducted a large-scale simulation study, in which our integrated models provided more accurate estimates of population trends than did Downing reconstruction. Estimates of trends were robust to various forms of model misspecification, including incorrectly specified cub and yearling survival parameters, age-related reporting biases in harvest data, and unmodeled temporal variability in survival and harvest rates. When applied to actual data on Minnesota black bears, the model predicted that harvest rates were negatively correlated with food availability and positively correlated with hunting effort, consistent with independent telemetry data. With no direct data on fertility, the model also correctly predicted 2-point cycles in cub production. Model-derived estimates of abundance for the most recent years provided a reasonable match to an empirical population estimate obtained after modeling efforts were completed.

Conclusions/Significance

Integrated population modeling provided a reasonable framework for synthesizing age-at-harvest data, periodic large-scale abundance estimates, and measured covariates thought to affect harvest rates of black bears in Minnesota. Collection and analysis of these data appear to form the basis of a robust and viable population monitoring program.

Bayesian analysis of wildlife age-at-harvest data

SUMMARY

State and federal natural resource management agencies often collect age-structured harvest data. These data represent finite realizations of stochastic demographic and sampling processes and have long been used by biologists to infer population trends. However, different sources of data have been combined in ad hoc ways and these methods usually failed to incorporate sampling error. In this article, we propose a "hidden process" (or state-space) model for estimating abundance, survival, recovery rate, and recruitment from age-at-harvest data that incorporate both demographic and sampling stochasticity. To this end, a likelihood for age-at-harvest data is developed by embedding a population dynamics model within a model for the sampling process. Under this framework, the identification of abundance parameters can be achieved by conducting a joint analysis with an auxiliary data set. We illustrate this approach by conducting a Bayesian analysis of age-at-harvest and mark-recovery data from black bears (Ursus americanus) in Pennsylvania. Using a set of reasonable prior distributions, we demonstrate a substantial increase in precision when posterior summaries of abundance are compared to a bias-corrected Lincoln-Petersen estimator. Because demographic processes link consecutive abundance estimates, we also obtain a more realistic biological picture of annual changes in abundance. Because age-at-harvest data are often readily obtained, we argue that this type of analysis provides a valuable strategy for wildlife population monitoring.

Adjusting age and stage distributions for misclassification errors

Ecologists often use samples from the age or stage structure of a population to make inferences about population-level processes and to parameterize matrix models. Typically, researchers make a simplifying assumption that age and stage classes are determined without error, when in fact some level of misclassification often can be expected. If unaccounted for, misclassification will lead to overly optimistic levels of precision and can cause biased estimates of age or stage structure. Although several studies have used information from known-age individuals to quantify errors in age or stage distribution, the problem of estimating the age or stage structure in face of such errors has received comparably little attention. In this paper, we describe a general statistical framework for estimating the true stage distribution of a sample when misclassification rates can be estimated. The estimation process requires auxiliary information on misclassification rates, such as data from individuals of known age. We analyze age-structured harvest records from black bears in Pennsylvania to illustrate how incorporating misclassification errors leads to changes in point estimates and provides a measure of precision.