Explore the vast range of titles available.

Florida Key deer (Odocoileus virginianus clavium) are an endangered subspecies of white-tailed deer endemic to the Lower Florida Keys. The New World screwworm (Cochliomyia hominivorax) infestation in July 2016 and Hurricane Irma on 10 September 2017 both caused the Key deer population to decline. Our objective was to estimate current Key deer population abundance using traditional distance sampling and a mark-resight methodology applicable for citizen scientist participation. For mark-resight efforts, deer were marked with hand sprayers using water-based livestock dye on Big Pine (BPK) and No Name keys (NNK). Biologists conducted road surveys between 9–13 March 2020 on BPK and NNK and collected data for mark retention, mark-resight, and distance calculations concurrently. Our mark-resight estimate (n = 748) was nearly 300 deer lower than the traditional distance estimate likely because of distance sampling's sensitivity to increased deer visibility along survey routes. Compared to historic data, our mark-resight population estimate indicated increased deer abundance compared to post-Hurricane Irma estimates (n = 573), but slightly below post-screwworm outbreak estimates (n = 860). Based on mark-retention data, we recommend all resight surveys be completed within 5 days of the first mark placement for the most dependable mark detection. We recommend our mark-resight method be used in future Key deer surveys as it is simple, efficient, and can bereliably completed with the assistance of volunteers therefore allowing for more regular monitoring.

We flew aerial line-transect surveys to estimate the range-wide population size of lesser prairie-chickens (Tympanuchus pallidicinctus) in the Great Plains, United States in 2012 and 2013. We also estimated the number of lesser prairie-chicken leks, the number of mixed-species leks that contained both lesser and greater prairie-chickens (T. cupido) and the number of hybrid lesser–greater prairie-chickens where these species' ranges overlap. The study area included the 2011 estimated occupied lesser prairie-chicken range in 5 states and was divided into 4 ecoregions. We created a sampling frame over the study area, consisting of 536 15- × 15-kmgrid cells. We flew 512 transects within a probabilistic sample of 256 cells totaling 7,680 km in 2012 and 566 transects within a probabilistic sample of 283 cells totaling 8,490 km in 2013. We estimated a total of 34,440 individual lesser prairie-chickens in 2012 (17,615 in 2013) and 350 hybrid lesser–greater prairie-chickens in 2012 (342 in 2013) in the study area. We estimated a total of 2,930 lesser prairie-chicken leks in 2012 (2,037 in 2013) and 453 lesser and greater prairie-chicken mixed leks in 2012 (356 in 2013) in the study area. We discuss the implications of alternative sampling designs with regard to conservation questions to be addressed.

Estimating densities of cougar (Puma concolor) is important for managing cougars and their prey but remains challenging because of cougar's elusive and solitary behavior. To evaluate a non-invasive, genetic capture-recapture method to estimate cougar population size and density, we surveyed a 220-km² area using conservation detection dogs trained to locate scat over a 4-week sampling period in northeast Oregon. We collected 272 scat samples and conducted DNA analysis on 249 samples from which we determined individual identification from 73 samples that represented 21 cougars (9 males and 12 females). We evaluated 4 models to estimate cougar densities: Huggins closed population capture-recapture (Huggins), CAPWIRE, multiple detections with Poisson (MDP), and spatially explicit capture-recapture (SECR). Population estimates for cougars using our study area were 26 (95% CI = 22-35, 9 males and 17 females) from Huggins models, 24 (95% CI = 21-30, 9 males and 15 females) from CAPWIRE, and 27 (95% CI = 24-42, 9 males and 18 females) from the MDP model. We accounted for the edge effect in density estimates caused by individuals whose home ranges included only a portion of the survey grid by buffering the study area using the mean home range radius of 8 cougars equipped with global positioning system collars on or near the study area. We estimated densities of 4.6 cougars/100 km² (95% CI = 3.8-8.3) for the Huggins model, 4.8 cougars/100 km² (95% CI = 4.2-7.8) for the MDP model, 4.2 cougars/100 km² (95% CI = 3.3-5.3) for the CAPWIRE model, and 5.0 cougars/100 km² (95% CI = 3.2-7.7) for the SECR model. Our results suggested estimating cougar densities using scat detection dogs could be feasible at a broader scale with less effort than other methods currently being used.

Heterogeneity in detection probabilities has long been recognized as problematic in mark-recapture studies, and numerous models developed to accommodate its effects. Individual heterogeneity is especially problematic, in that reasonable alternative models may predict essentially identical observations from populations of substantially different sizes. Thus even with very large samples, the analyst will not be able to distinguish among reasonable models of heterogeneity, even though these yield quite distinct inferences about population size. The problem is illustrated with models for closed and open populations.

In the context of rapid urbanization, the refined management of cities is facing higher requirements. In improving urban population management levels and the scientific allocation of resources, fine-scale population data plays an increasingly important role. The current population estimation studies mainly focus on low spatial resolution, such as city-scale and county scale, without considering differences in population distributions within cities. This paper mines and defines the spatial correlations of multi-source data, including urban building data, point of interest (POI) data, census data, and administrative division data. With populations mainly distributed in residential buildings, a population estimation model at a subdistrict scale is established based on building classifications. Composed of spatial information and attribute information, POI data are spaced irregularly. Based on this characteristic, the text classification method, frequency-inverse document frequency (TF-IDF), is applied to obtain functional classifications of buildings. Then we screen out residential buildings, and quantify characteristic variables in subdistricts, including perimeter, area, and total number of floors in residential buildings. To assess the validity of the variables, the random forest method is selected for variable screening and correlation analysis, because this method has clear advantages when dealing with unbalanced data. Under the assumption of linearity, multiple regression analysis is conducted, to obtain a linear model of the number of buildings, their geometric characteristics, and the population in each administrative division. Experiments showed that the urban fine-scale population estimation model established in this study can estimate the population at a subdistrict scale with high accuracy. This method improves the precision and automation of urban population estimation. It allows the accurate estimation of the population at a subdistrict scale, thereby providing important data to support the overall planning of regional energy resource allocation, economic development, social governance, and environmental protection.

Observation bias pervades data collected during aerial surveys of large animals, and although some sources can be mitigated with informed planning, others must be addressed using valid sampling techniques that carefully model detection probability. Nonetheless, aerial surveys are frequently employed to count large mammals without applying such methods to account for heterogeneity in visibility of animal groups on the landscape. This often leaves managers and interest groups at odds over decisions that are not adequately informed. I analyzed detection of feral horse (Equus caballus) groups by dual independent observers from 24 fixed-wing and 16 helicopter flights using mixed-effect logistic regression models to investigate potential sources of observation bias. I accounted for observer skill, population location, and aircraft type in the model structure and analyzed the effects of group size, sun effect (position related to observer), vegetation type, topography, cloud cover, percent snow cover, and observer fatigue on detection of horse groups. The most important model-averaged effects for both fixed-wing and helicopter surveys included group size (fixed-wing: odds ratio = 0.891, 95% CI = 0.850-0.935; helicopter: odds ratio = 0.640, 95% CI = 0.587-0.698) and sun effect (fixed-wing: odds ratio = 0.632, 95% CI = 0.350-1.141; helicopter: odds ratio = 0.194, 95% CI = 0.080-0.470). Observer fatigue was also an important effect in the best model for helicopter surveys, with detection probability declining after 3 hr of survey time (odds ratio = 0.278,95% CI = 0.144-0.537). Biases arising from sun effect and observer fatigue can be mitigated by pre-flight survey design. Other sources of bias, such as those arising from group size, topography, and vegetation can only be addressed by employing valid sampling techniques such as double sampling, mark-resight (batch-marked animals), mark-recapture (uniquely marked and identifiable animals), sightability bias correction models, and line transect distance sampling; however, some of these techniques may still only partially correct for negative observation biases.

In 1988–1989, 32 bobcats Lynx rufus were reintroduced to Cumberland Island (CUIS), Georgia, USA, from which they had previously been extirpated. They were monitored intensively for 3 years immediately post‐reintroduction, but no estimation of the size or genetic diversity of the population had been conducted in over 20 years since reintroduction. We returned to CUIS in 2012 to estimate abundance and effective population size of the present‐day population, as well as to quantify genetic diversity and inbreeding. We amplified 12 nuclear microsatellite loci from DNA isolated from scats to establish genetic profiles to identify individuals. We used spatially explicit capture–recapture population estimation to estimate abundance. From nine unique genetic profiles, we estimate a population size of 14.4 (SE = 3.052) bobcats, with an effective population size (Ne) of 5–8 breeding individuals. This is consistent with predictions of a population viability analysis conducted at the time of reintroduction, which estimated the population would average 12–13 bobcats after 10 years. We identified several pairs of related bobcats (parent‐offspring and full siblings), but ~75% of the pairwise comparisons were typical of unrelated individuals, and only one individual appeared inbred. Despite the small population size and other indications that it has likely experienced a genetic bottleneck, levels of genetic diversity in the CUIS bobcat population remain high compared to other mammalian carnivores. The reintroduction of bobcats to CUIS provides an opportunity to study changes in genetic diversity in an insular population without risk to this common species. Opportunities for natural immigration to the island are limited; therefore, continued monitoring and supplemental bobcat reintroductions could be used to evaluate the effect of different management strategies to maintain genetic diversity and population viability. The successful reintroduction and maintenance of a bobcat population on CUIS illustrates the suitability of translocation as a management tool for re‐establishing felid populations. In 1988–1989, 32 bobcats Lynx rufus were reintroduced to Cumberland Island, Georgia, USA, from which they had previously been extirpated. We returned to Cumberland Island in 2012 to estimate abundance and effective population size of the present‐day bobcat population, as well as to quantify genetic diversity and inbreeding. We estimate a current census population size of only 14.4 individuals, with an effective population size (Ne) of 5–8 breeding individuals; however, levels of genetic diversity remain high compared to other mammalian carnivores.

The North Cascades (Nooksack) elk (Cervus elaphus) population declined during the 1980s, prompting a closure to state and tribal hunting in 1997 and an effort to restore the herd to former abundance. In 2005, we began a study to assess the size of the elk population, judge the effectiveness of restoration efforts, and develop a practical monitoring strategy. We concurrently evaluated 2 monitoring approaches: sightability correction modeling and mark-resight modeling. We collected data during February—April helicopter surveys and fit logistic regression models to predict the sightability of elk groups based on group and environmental variables. We used an information-theoretic criterion to compare 9 models of varying complexity; the best model predicted sightability of elk groups based on 1) transformed (log 2 ) group size, 2) forest canopy cover (%), and 3) a categorical activity variable (active vs. bedded). The sightability model indicated relatively steady and modest herd growth during 2006—2011, but estimates were less than minimum-known-alive counts. We also used the logit-normal mixed effects (LNME) mark-resight model to generate estimates of total elk population size and the sizes of the adult female and branch-antlered male subpopulations. We explored 15 LNME models to predict total population size and 12 models to predict subpopulations. Our results indicated individual heterogeneity in resighting probabilities and variation in resighting probabilities across sexes and some years. Model-averaged estimates of total population size increased from 639 (95% CI = 570—706) in spring 2006 to 1,248 (95% CI = 1,094—1,401) in 2011. We estimated the adult female subpopulation increased from 381 (95% CI = 338—424) in spring 2006 to 573 (95% CI = 507—639) by 2011. The branch-antlered male subpopulation estimates increased from 87 (95% CI = 54—119) to 180 (95% CI = 118—241) from spring 2006 to spring 2011. The LNME model estimates were greater than sightability model estimates and minimum-known-alive counts. We concluded that mark-resight performed better and was a viable approach for monitoring this small elk population and possibly others with similar characteristics (i.e., small population and landscape scales), but this approach requires periodic marking of elk; we estimated mark-resight costs would be about 40% greater than sightability model application costs. The utility of sightability-correction modeling was limited by a high proportion of groups with low detectability on our densely forested landscape.

Although genetic and analytical methods for estimating wildlife abundance have improved rapidly over the last decade, effective methods for collecting hair samples from terrestrial carnivores in a mark–recapture framework have lagged. Hair samples are generally collected using methods that permit sampling of multiple individuals during a single sampling period that can cause genotyping errors due to cross-contamination. We evaluated a modified body snare as a single-sample method to obtain bobcat hair samples suitable for individual identification using DNA analyses to estimate population size. We used a systematic grid (2.5 × 2.5 km) overlaid on a 278.5 km2study area in Michigan's Upper Peninsula to distribute sampling effort. In each of 44 grid cells, we placed 2–6 snares at established sampling stations and collected hair samples weekly for 8 weeks during January–March 2010. We collected 230 hair samples overall, with 91% of sampling stations obtaining at least 1 hair sample. Fifty-seven percent of samples had sufficient DNA for species identification, which included bobcat (Lynx rufus, n= 17); raccoon (Procyon lotor, n= 62); coyote, dog, or wolf (Canisspp.,n= 29); fox (Vulpes vulpesorUrocyon cinereoargenteus, n= 4); and fisher (Martes pennanti, n= 1). We identified 8 individual bobcats and using Huggins closed capture population models with a one-half mean maximum distance moved buffer, estimated 10 individuals within the trapping area (95% confidence interval = 8–28) with a density of 3.0 bobcats/100 km2. Our method provides an effective, single-sample technique for detecting bobcats and estimating abundance.

Illegal wildlife trade has reached alarming levels globally, extirpating populations of commercially valuable species. As a driver of biodiversity loss, quantifying illegal harvest is essential for conservation and sociopolitical affairs but notoriously difficult. Here we combine field-based carcass monitoring with fine-scale demographic data from an intensively studied wild African elephant population in Samburu, Kenya, to partition mortality into natural and illegal causes. We then expand our analytical framework to model illegal killing rates and population trends of elephants at regional and continental scales using carcass data collected by a Convention on International Trade in Endangered Species program. At the intensively monitored site, illegal killing increased markedly after 2008 and was correlated strongly with the local black market ivory price and increased seizures of ivory destined for China. More broadly, results from application to continental data indicated illegal killing levels were unsustainable for the species between 2010 and 2012, peaking to ~8% in 2011 which extrapolates to ~40,000 elephants illegally killed and a probable species reduction of ~3% that year. Preliminary data from 2013 indicate o ver harvesting continued. In contrast to the rest of Africa, our analysis corroborates that Central African forest elephants experienced decline throughout the last decade. These results provide the most comprehensive assessment of illegal ivory harvest to date and confirm that current ivory consumption is not sustainable. Further, our approach provides a powerful basis to determine cryptic mortality and gain understanding of the demography of at-risk species.