Ape Population Abundance Estimates

This annex presents ape abundance estimates at the site level. The term “site” refers to a protected area and its buffer zone, a logging concession or group of concessions, or any discrete area where a survey has taken place in the past two decades, although this annex also lists a few sites that were last surveyed in the 1970s and 1980s.Output Type: Online-only anne

On population abundance and niche structure

Recent published evidence indicates a negative correlation between density of populations and the distance of their environments to a suitably defined ‘niche centroid’. This empirical observation lacks theoretical grounds. We provide a theoretical underpinning for the empirical relationship between population density and position in niche space, and use this framework to understand the circumstances under which the relationship will fail. We propose a metapopulation model for the area of distribution, as a system of ordinary differential equations coupled with a dispersal kernel. We present an analytical approximation to the solution of the system as well as R code to solve the full model numerically. We use this tool to analyze various scenarios and assumptions. General and realistic demographic assumptions imply a good correlation between position in niche space and population abundance. Factors that modify this correlation are: transitory states, a heterogeneous spatial structure of suitability, and Allee effects. We also explain why the raw output of the niche modeling algorithm MaxEnt is not a good predictor of environmental suitability. Our results elucidate the empirical results for spatial patterns of population size in niche terms, and provide a theoretical basis for a structured theory of the niche

Estimating rodent population abundance using early climatic predictors

Climate might directly or indirectly affect the population dynamics of several rodent species including Apodemus flavicollis, a very common forest small mammal and an important reservoir for several emerging zoonotic pathogens. We thus investigated how climatic data alone might be useful to predict rodent population dynamics. We used rodent data gathered through a long-term monitoring effort carried out for 17 years (2000–2017) using a capture-mark-recapture method in northern Italy. Temperature and precipitation data were obtained from a weather station close to the study area. Linear models were implemented to assess how mice density was associated with weather conditions considering various time lags. We found that warmer summers 2 years before sampling were positively related to A. flavicollis annual average population densities. Conversely, precipitation occurring the autumn 1 year before sampling negatively influenced mice abundance. To the best of our knowledge, this is one of the first attempts at investigating how rodent abundance is associated with climatic conditions in the central European region of the Alps. Our results highlight important correlations, which eventually might be used for estimating risk of transmission of rodent-borne zoonotic pathogen

Estimating Population Abundance Using Sightability Models: R SightabilityModel Package

Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population surveys (Steinhorst and Samuel 1989). Estimation proceeds in 2 stages: (1) Sightability trials are conducted with marked individuals, and logistic regression is used to estimate the probability of detection as a function of available covariates (e.g., visual obstruction, group size). (2) The fitted model is used to adjust counts (from future surveys) for animals that were not observed. A modified Horvitz-Thompson estimator is used to estimate abundance: counts of observed animal groups are divided by their inclusion probabilites (determined by plot-level sampling probabilities and the detection probabilities estimated from stage 1). We provide a brief historical account of the approach, clarifying and documenting suggested modifications to the variance estimators originally proposed by Steinhorst and Samuel (1989). We then introduce a new R package, SightabilityModel, for estimating abundance using this technique. Lastly, we illustrate the software with a series of examples using data collected from moose (Alces alces) in northeastern Minnesota and mountain goats (Oreamnos americanus) in Washington State

Uncertainty-Aware Estimation of Population Abundance using Machine Learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classication based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classication. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is ne

Population Abundance and Transience of Selected Coastal Plain Crayfishes

Crayfish are important for stability, determining ecosystem structure, and ecosystem functions in freshwater habitats. Louisiana has many endemic species of crayfish, but most are poorly described. This study investigated the populations of some of the lesser known crayfishes in the South Central Plains. Specifically, the goals were to examine movement, movement across anthropogenic barriers, and estimate population size of 10 species reported from the area: Procambarus natchitochae, P. vioscai, P. clarkii, P. acutus, P. zonangulus, P. tulanei, P. kensleyi, Orconectes maletae, O. lancifer, and O. palmeri. In combination with a field team, I sampled twelve wadeable streams with DC backpack electrofishers and traps at least twice at 2-3 month intervals during summer, fall, and winter of 2014. Although all sampled crayfish of sufficient size were double marked, recaptures were minimal, thus, generalized N-mixture models were performed on the three most widely captured species to generate population and transience estimates based on sampling unmarked animals over time. All population estimates were very low and were dependent on river basin, specific conductance, and stream width. Although the relationship among species and river basins has been previously described, relationships with stream size and specific conductance were novel. P. natchitochae and P. vioscai appeared to spatially segregate along a gradient of stream size. Specific conductance, which is an indicator of available calcium, had a positive association with abundance for P. vioscai, P. natchitochae. P. natchitochae and P. vioscai showed the possibility of seasonal transience and potential relationships with dissolved oxygen. These results give conservation scientists and managers more information for conservation of Louisiana crayfishes

The spatio-temporal distribution patterns of biting midges of the genus Culicoides in Salta province, Argentina

The goal of this survey was to analyze the spatio-temporal distribution patterns of Culicoides Latreille species (Diptera: Ceratopogonidae) and their relationship with environmental variables in Salta, northwestern Argentina. Culicoides were collected monthly from January 2003 through December 2005. The influence of the climatic variables on population abundance was analyzed with a multilevel Poisson regression. A total of 918 specimens belonging to five species were collected. The most abundant species was Culicoides paraensis Goeldi (65.5%), followed by Culicoides lahillei Iches (14.6%) and Culicoides debilipalpis Lutz (7.6%). The highest seasonal abundance for C. paraensis, C. debilipalpis and C. lahillei occurred during the spring and summer. A Poisson regression analysis showed that the mean maximum and minimum temperature and the mean maximum and minimum humidity were the variables with the greatest influence on the population abundance of Culicoides species.Fil: Veggiani Aybar, Cecilia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto Superior de Entomología; ArgentinaFil: Dantur Juri, Maria Julia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto Superior de Entomología; Argentina. Universidad Nacional de Chilecito; ArgentinaFil: Santana, Mirta Sara. Universidad Nacional de Tucuman. Facultad de Medicina. Departamento de Investigación. Area de Bioestadística; ArgentinaFil: Lizarralde, Mercedes Sara. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto Superior de Entomología; ArgentinaFil: Spinelli, Gustavo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Limnología "Dr. Raúl A. Ringuelet". Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Instituto de Limnología; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Entomología; Argentin

Multifactorial Uncertainty Assessment for Monitoring Population Abundance using Computer Vision

Computer vision enables in-situ monitoring of animal populations at a lower cost and with less ecosystem disturbance than with human observers. However, computer vision uncertainty may not be fully understood by end-users, and the uncertainty assessments performed by technology experts may not fully address end-user needs. This knowledge gap can yield misinterpretations of computer vision data, and trust issues impeding the transfer of valuable technologies. We bridge this gap with a user-centered analysis of the uncertainty issues. Key uncertainty factors, and their interactions, are identified from the perspective of a core task in ecology research and beyond: counting individuals from different classes. We highlight factors for which uncertainty assessment methods are currently unavailable. The remaining uncertainty assessment methods are not interoperable. Hence it is currently difficult to assess the combined results of multiple uncertainty factors, and their impact on end-user counting tasks. We propose a framework for assessing the multifactorial uncertainty propagation along the data processing pipeline. It integrates methods from both computer vision and ecology domains, and aims at supporting the statistical analysis of abundance trends for population monitoring. Our typology of uncertainty factors and our assessment methods were drawn from interviews with marine ecology and computer vision experts, and from prior work for a fish monitoring application. Our findings contribute to enabling scientific research based on computer vision