Black and Brown Bear Density Estimates Using Modified Capture-Recapture Techniques in Alaska

1987 ◽  
Vol 7 ◽  
pp. 23
Author(s):  
Sterling D. Miller ◽  
Earl F. Becker ◽  
Warren B. Ballard
Keyword(s):  
Brown Bear ◽  
Density Estimates ◽  
Capture Recapture

scholarly journals Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture-recapture modelling

2021 ◽  
Author(s):  
Soumen Dey ◽  
Richard Bischof ◽  
Pierre P. A. Dupont ◽  
Cyril Milleret
Keyword(s):  
Home Range ◽  
Space Use ◽  
Relative Bias ◽  
Diagnostic Tools ◽  
List Type ◽  
Detection Function ◽  
Density Estimates ◽  
Use Patterns ◽  
Wide Range ◽  
Capture Recapture

AbstractSpatial capture-recapture (SCR) is now used widely to estimate wildlife densities. At the core of SCR models lies the detection function, linking individual detection probability to the distance from its latent activity center. The most common function (half-normal) assumes a bivariate normal space use and consequently detection pattern. This is likely an oversimplification and misrepresentation of real-life animal space use patterns, but studies have reported that density estimates are relatively robust to misspecified detection functions. However, information about consequences of such misspecification on space use parameters (e.g. home range area), as well as diagnostic tools to reveal it are lacking.We simulated SCR data under six different detection functions, including the half-normal, to represent a wide range of space use patterns. We then fit three different SCR models, with the three simplest detection functions (half-normal, exponential and half-normal plateau) to each simulated data set. We evaluated the consequences of misspecification in terms of bias, precision and coverage probability of density and home range area estimates. We also calculated Bayesian p-values with respect to different discrepancy metrics to assess whether these can help identify misspecifications of the detection function.We corroborate previous findings that density estimates are robust to misspecifications of the detection function. However, estimates of home range area are prone to bias when the detection function is misspecified. When fitted with the half-normal model, average relative bias of 95% kernel home range area estimates ranged between −25% and 26% depending on the misspecification. In contrast, the half-normal plateau model (an extension of the half-normal) returned average relative bias that ranged between −26% and −4%. Additionally, we found useful heuristic patterns in Bayesian p-values to diagnose the misspecification in detection function.Our analytical framework and diagnostic tools may help users select a detection function when analyzing empirical data, especially when space use parameters (such as home range area) are of interest. We urge development of additional custom goodness of fit diagnostics for Bayesian SCR models to help practitioners identify a wider range of model misspecifications.

scholarly journals Density Estimates of Unmarked Large Mammals at Camera Traps Vary among Models, Species, and Years, Signalling Importance of Model Assumptions

2021 ◽  
Author(s):  
Jason Fisher ◽  
Joanna Burgar ◽  
Melanie Dickie ◽  
Cole Burton ◽  
Rob Serrouya
Keyword(s):  
Density Estimation ◽  
Black Bear ◽  
Camera Traps ◽  
Mammal Species ◽  
Density Estimates ◽  
Movement Rates ◽  
Social Species ◽  
Marked Variability ◽  
Capture Recapture ◽  
Best Fitting

Density estimation is a key goal in ecology but accurate estimates remain elusive, especially for unmarked animals. Data from camera-trap networks combined with new density estimation models can bridge this gap but recent research has shown marked variability in accuracy, precision, and concordance among estimators. We extend this work by comparing estimates from two different classes of models: unmarked spatial capture-recapture (spatial count, SC) models, and Time In Front of Camera (TIFC) models, a class of random encounter model. We estimated density for four large mammal species with different movement rates, behaviours, and sociality, as these traits directly relate to model assumptions. TIFC density estimates were typically higher than SC model estimates for all species. Black bear TIFC estimates were ~ 10-fold greater than SC estimates. Caribou TIFC estimates were 2-10 fold greater than SC estimates. White-tailed deer TIFC estimates were up to 100-fold greater than SC estimates. Differences of 2-5 fold were common for other species in other years. SC estimates were annually stable except for one social species; TIFC estimates were highly annually variable in some cases and consistent in others. Tests against densities obtained from DNA surveys and aerial surveys also showed variable concordance and divergence. For gregarious animals TIFC may outperform SC due to the latter model’s assumption of independent activity centres. For curious animals likely to investigate camera traps, SC may outperform TIFC, which assumes animal behavior is unaffected by cameras. Unmarked models offer great possibilities, but a pragmatic approach employs multiple estimators where possible, considers the ecological plausibility of assumptions, and uses an informed multi-inference approach to seek estimates from models with assumptions best fitting a species’ biology.

scholarly journals Estimating abundance of a recovering transboundary brown bear population with capture-recapture models

2021 ◽  
Author(s):  
Cecile Vanpe ◽  
Blaise Piedallu ◽  
Pierre-Yves Quenette ◽  
Jerome Sentilles ◽  
Guillaume Queney ◽  
...  
Keyword(s):  
Population Size ◽  
Conservation Status ◽  
Brown Bear ◽  
Home Range Size ◽  
Management Decision ◽  
Lower Survival Rate ◽  
Population Size Estimates ◽  
Management Decision Making ◽  
Annual Population ◽  
Capture Recapture

Abundance of small populations of large mammals may be assessed using complete counts of the different individuals detected over a time period, so-called minimum detected size (MDS). However, as population is growing larger and its distribution is expanding wider, the risk of under-estimating population size using MDS is increasing sharply due to the rarely fulfilled assumption of perfect detection of all individuals of the population, and as a result, the need to report uncertainty in population size estimates becomes crucial. We addressed these issues within the framework of the monitoring of the critically endangered Pyrenean brown bear population that was on the edge of extinction in the mid-1990s with only five individuals remaining, but was reinforced by 11 bears originated from Slovenia since then. We used Pollock's closed robust design (PCRD) capture-recapture models applied to the cross-border non-invasive sampling data from France, Spain and Andorra to provide the first published annual abundance estimates of the Pyrenean brown bear population and its trends over time. Annual population size increased and displayed a fivefold rise between 2008 and 2020, reaching > 60 individuals in 2020. Detection heterogeneity among individuals may stem from intraspecific home range size disparities making it more likely to find signs of individuals who move more. We found a lower survival rate in cubs than in adults and subadults, since the formers suffer from more mortality risks (such as infanticides, predations, mother death or abandonments) than the latters. Our study provides evidence that the PCRD capture-recapture modelling approach can provide reliable estimates of the size of and trend in large mammal populations, while minimizing bias due to inter-individual heterogeneity in detection probabilities and allowing the quantification of sampling uncertainty surrounding these estimates. Such information is vital for informing management decision-making and assessing population conservation status.

Evaluating Live trapping and Camera-based Indices of Small Mammal Density

2021 ◽  
Author(s):  
Mitchell Alan Parsons ◽  
ALISHIA ORLOFF ◽  
Laura Prugh
Keyword(s):  
Small Mammal ◽  
Abundant Species ◽  
Monitoring Methods ◽  
Density Estimates ◽  
Full Dataset ◽  
Live Trapping ◽  
Minimum Number ◽  
Capture Recapture ◽  
Two Indices ◽  
Best Fit

Density estimates are integral to wildlife management, but they can be costly to obtain. Indices of density may provide efficient alternatives, but calibration is needed to ensure the indices accurately reflect density. We evaluated several indices of small mammal density using live trapping and motion-activated cameras in Washington’s Cascade Mountains. We used linear regression to compare spatially-explicit capture recapture density estimates of mice, voles, and chipmunks to four indices. Two indices were based on live trapping (minimum number alive and number of captures per 100 trap nights) and two indices were based on photos from motion-activated cameras (proportion of cameras detecting a species and the number of independent detections). We evaluated how the accuracy of trap-based indices increased with trapping effort using subsets of the full dataset (n = 7 capture occasions per site). Most indices provided reliable indicators of small mammal density, and live trapping indices (R2=0.64 – 0.98) outperformed camera-based indices (R2=0.24 – 0.86). All indices performed better for more abundant species. The effort required to estimate each index varied, and indices that required more effort performed better. These findings should help managers, conservation practitioners, and researchers select small mammal monitoring methods that best fit their needs.

scholarly journals Using bear rub data and spatial capture-recapture models to estimate trend in a brown bear population

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Katherine C. Kendall ◽  
Tabitha A. Graves ◽  
J. Andrew Royle ◽  
Amy C. Macleod ◽  
Kevin S. McKelvey ◽  
...  
Keyword(s):  
Growth Rates ◽  
Population Change ◽  
Local Density ◽  
Brown Bear ◽  
Rate Of Change ◽  
Grizzly Bear ◽  
Conservation Area ◽  
The North ◽  
Capture Recapture ◽  
Genetic Detection

AbstractTrends in population abundance can be challenging to quantify during range expansion and contraction, when there is spatial variation in trend, or the conservation area is large. We used genetic detection data from natural bear rubbing sites and spatial capture-recapture (SCR) modeling to estimate local density and population growth rates in a grizzly bear population in northwestern Montana, USA. We visited bear rubs to collect hair in 2004, 2009—2012 (3,579—4,802 rubs) and detected 249—355 individual bears each year. We estimated the finite annual population rate of change 2004—2012 was 1.043 (95% CI = 1.017—1.069). Population density shifted from being concentrated in the north in 2004 to a more even distribution across the ecosystem by 2012. Our genetic detection sampling approach coupled with SCR modeling allowed us to estimate spatially variable growth rates of an expanding grizzly bear population and provided insight into how those patterns developed. The ability of SCR to utilize unstructured data and produce spatially explicit maps that indicate where population change is occurring promises to facilitate the monitoring of difficult-to-study species across large spatial areas.

scholarly journals Bringing clarity to the clouded leopard Neofelis diardi: first density estimates from Sumatra

Oryx ◽  
2014 ◽  
Vol 48 (4) ◽  
pp. 536-539 ◽  
Author(s):  
Rahel Sollmann ◽  
Matthew Linkie ◽  
Iding A. Haidir ◽  
David W. Macdonald
Keyword(s):  
Mixed Forest ◽  
Camera Trap ◽  
Forest Loss ◽  
Data Sets ◽  
Panthera Tigris ◽  
Density Estimates ◽  
Clouded Leopard ◽  
Data Limitations ◽  
Capture Recapture ◽  
Study Sites

AbstractWe use data from camera-trap surveys for tigers Panthera tigris in combination with spatial capture–recapture models to provide the first density estimates for the Sunda clouded leopard Neofelis diardi on Sumatra. Surveys took place during 2004–2007 in the Kerinci landscape. Densities were 0.385–1.278 per 100 km2. We found no statistically significant differences in density among four study sites or between primary and mixed forest. Because the data sets are too small to account for differences in detection parameters between sexes, density is probably underestimated. Estimates are comparable to previous estimates of 1–2 per 100 km2 from the lowlands of central Sabah, on Borneo. Data limitations suggest that camera-trap surveys for Sunda clouded leopards require traps spaced more closely, to increase the chance of recaptures at different traps. Nevertheless, these first density estimates for clouded leopards on Sumatra provide a benchmark for measuring future conservation impact on an island that is undergoing rapid forest loss.

Addressing challenges in non invasive capture-recapture based estimates of small populations: a pilot study on the Apennine brown bear

2010 ◽  
Vol 11 (6) ◽  
pp. 2299-2310 ◽  
Author(s):  
V. Gervasi ◽  
P. Ciucci ◽  
F. Davoli ◽  
J. Boulanger ◽  
L. Boitani ◽  
...  
Keyword(s):  
Pilot Study ◽  
Brown Bear ◽  
Small Populations ◽  
Non Invasive ◽  
Capture Recapture ◽  
Apennine Brown Bear

scholarly journals Density of the Vulnerable Sunda clouded leopard Neofelis diardi in two commercial forest reserves in Sabah, Malaysian Borneo

Oryx ◽  
2012 ◽  
Vol 46 (3) ◽  
pp. 423-426 ◽  
Author(s):  
Andreas Wilting ◽  
Azlan Mohamed ◽  
Laurentius N. Ambu ◽  
Peter Lagan ◽  
Sam Mannan ◽  
...  
Keyword(s):  
Management Practices ◽  
Iucn Red List ◽  
Separate Species ◽  
Density Estimates ◽  
Clouded Leopard ◽  
Forest Reserves ◽  
Protected Forests ◽  
Capture Recapture ◽  
Commercial Forest ◽  
Malaysian Borneo

AbstractRecently the Sunda clouded leopard Neofelis diardi was recognized as a separate species distinct from the clouded leopard Neofelis nebulosa of mainland Asia. Both species are categorized as Vulnerable on the IUCN Red List. Little is known about the newly identified species and, in particular, information from forests outside protected areas is scarce. Here we present one of the first density estimates calculated with spatial capture–recapture models using camera-trap data. In two commercial forest reserves in Sabah (both certified for their sustainable management practices) the density of the Sunda clouded leopard was estimated to be c. 1 per 100 km2 (0.84±SE 0.42 and 1.04±SE 0.58). The presence of the Sunda clouded leopard in such forests is encouraging for its conservation but additional studies from other areas, including protected forests, are needed to compare and evaluate these densities.

scholarly journals How Does Spatial Study Design Influence Density Estimates from Spatial Capture-Recapture Models?

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34575 ◽  
Author(s):  
Rahel Sollmann ◽  
Beth Gardner ◽  
Jerrold L. Belant
Keyword(s):  
Study Design ◽  
Density Estimates ◽  
Capture Recapture

scholarly journals Assumptions about fence permeability influence density estimates for brown hyaenas across South Africa

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kathryn S. Williams ◽  
Samual T. Williams ◽  
Rebecca J. Welch ◽  
Courtney J. Marneweck ◽  
Gareth K. H. Mann ◽  
...  
Keyword(s):  
South Africa ◽  
Population Density ◽  
Wildlife Management ◽  
Management Decisions ◽  
Density Estimates ◽  
Future Studies ◽  
Dominant Feature ◽  
Capture Recapture ◽  
Number Of Individuals ◽  
Density Results

AbstractWildlife population density estimates provide information on the number of individuals in an area and influence conservation management decisions. Thus, accuracy is vital. A dominant feature in many landscapes globally is fencing, yet the implications of fence permeability on density estimation using spatial capture-recapture modelling are seldom considered. We used camera trap data from 15 fenced reserves across South Africa to examine the density of brown hyaenas (Parahyaena brunnea). We estimated density and modelled its relationship with a suite of covariates when fenced reserve boundaries were assumed to be permeable or impermeable to hyaena movements. The best performing models were those that included only the influence of study site on both hyaena density and detection probability, regardless of assumptions of fence permeability. When fences were considered impermeable, densities ranged from 2.55 to 15.06 animals per 100 km2, but when fences were considered permeable, density estimates were on average 9.52 times lower (from 0.17 to 1.59 animals per 100 km2). Fence permeability should therefore be an essential consideration when estimating density, especially since density results can considerably influence wildlife management decisions. In the absence of strong evidence to the contrary, future studies in fenced areas should assume some degree of permeability in order to avoid overestimating population density.

Sign in / Sign up

Export Citation Format

Share Document