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.

The Female Population Growth Projection Year 2021 in Trenggalek Regency by Leslie Matrix Model on the Birth Rate and Life Expectancy

<p>This research aims to determine the number of female residents in Trenggalek Regency in 2021 based on data on birth rate and life expectancy. The use of eigenvalues and eigenvectors aims to determine the dividing age distribution by Leslie matrix model. The eigenvectors are used to determine the number of female populations of each age interval, while the eigenvalues are used to determine population growth rates. The research method used is to determine the subject of research. The next stage is to collect research data, then analyze the data and last draw conclusions. The research data is obtained from BPS Kabupaten Trenggalek and BPS East Java Province that is data of woman population from year 2010-2015. The result of this research using Leslie matrix model for female population in Trenggalek Regency that is discrete model. The discrete model is divided into fourteen age intervals constructed using the birthrate and life expectancy. The conclusions of the study showed that the number of female population in Trenggalek Regency tended to increase with positive eigen value greater than one. In other words, the growth rate of female population in Trenggalek Regency tends to be positive. The success of Leslie's matrix model is the application of case studies in predicting the number of female populations in Trenggalek District by 2021 using the MAPLE 16 Program.</p>

An Analysis of a Semelparous Population Model with Density-Dependent Fecundity and Density-Dependent Survival Probabilities

A discrete age-structured semelparous Leslie matrix model where density dependence is included both in the fecundity and in the survival probabilities is analysed. Depending on strength of density dependence, we show in the precocious semelparous case that the nonstationary dynamics may indeed be rich, ranging from SYC (a dynamical state where the whole population is in one age class only) dynamics to cycles of low period where all age classes are populated. Quasiperiodic and chaotic dynamics have also been identified. Moreover, outside parameter regions where SYC dynamics dominates, we prove that the transfer from stability to instability goes through a supercritical Neimark−Sacker bifurcation, and it is further shown that when the population switches from possessing a precocious to a delayed semelparous life history both stability properties and the possibility of periodic dynamics become weaker.

An Approximate Bayesian Method Applied to Estimating the Trajectories of Four British Grey Seal (Halichoerus grypus) Populations from Pup Counts

For British grey seals, as with many pinniped species, population monitoring is implemented by aerial surveys of pups at breeding colonies. Scaling pup counts up to population estimates requires assumptions about population structure; this is straightforward when populations are growing exponentially but not when growth slows, since it is unclear whether density dependence affects pup survival or fecundity. We present an approximate Bayesian method for fitting pup trajectories, estimating adult population size and investigating alternative biological models. The method is equivalent to fitting a density-dependent Leslie matrix model, within a Bayesian framework, but with the forms of the density-dependent effects as outputs rather than assumptions. It requires fewer assumptions than the state space models currently used and produces similar estimates. We discuss the potential and limitations of the method and suggest that this approach provides a useful tool for at least the preliminary analysis of similar datasets.

The stock recovery rate in a Central African rain forest: an index of sustainability based on projection matrix models

The stock recovery rate is used in most natural forests of the Congo Basin to assess logging sustainability. This rate is computed using the so-called Dimako formula. Although this formula has been used for many years now in management plans, its mathematical properties have not been closely reviewed. We show that the Dimako formula corresponds to a Leslie matrix model, and then we propose an extension of it as a Usher matrix model. The stock recovery rate at the end of the first felling cycle for six commercial species in the Central African Republic varied between 21.7% and 99.9%. As felling cycles follow each other, the stock recovery rate converged towards a limit that is the asymptotic stock recovery rate. This limit varies between 27.2% and 158.4% for the same six species. Comparing felling scenarios reveals that increasing the minimum harvest diameter was as efficient at increasing the stock recovery rate at the end of the first felling cycle as decreasing the logging intensity. The results for the other parameters of the felling scenarios varied among species, with changes in the stock recovery rate ranging from 0% to 180% at the end of the first felling cycle, and changes in the asymptotic rate ranging from 0% to 685%.