Accuracy of some aerial survey estimators: contrasts with known numbers

2008 ◽  
Vol 35 (4) ◽  
pp. 377 ◽  
Author(s):  
John P. Tracey ◽  
Peter J. S. Fleming ◽  
Gavin J. Melville
Keyword(s):  
Detection Probability ◽  
Aerial Survey ◽  
Capra Hircus ◽  
True Density ◽  
Density Estimates ◽  
Factors Influencing ◽  
Capture Recapture ◽  
Wildlife Populations ◽  
Helicopter Survey ◽  
Feral Goat

Density estimates are seldom examined against actual population size, hence the ability of estimators to correct for bias is unknown. Studies that compare techniques are difficult to interpret because of the uncertainty of adherence to their respective assumptions. Factors influencing detection probability, estimators that correct for bias, the validity of their assumptions and how these relate to true density are important considerations for selecting suitable methods. Here we contrasted five estimates of feral goat (Capra hircus) densities obtained from aerial surveys (strip counts, Petersen, stratified Petersen, Chao, Alho) against known densities derived from total counts. After correcting for recounting, the Alho and stratified Petersen estimators applied to helicopter surveys were the most accurate (bias = 0.08 and –0.09 respectively), which suggests that estimates were improved by correcting individual observations according to the characteristics of each observation. An approach using modified Horvitz–Thompson equations for unequal-sized units is described and is recommended to allow for this. Both the Chao (bias = 0.35) and Petersen (bias = 0.22) estimators were positively biased, which is likely to be a consequence of averaging detection probability across all observations. Helicopter survey using capture–recapture with multiple observers is recommended for estimating the density of wildlife populations. However, adjustment for the factors that influence detection probability is required.

Does variable probability of detection compromise the use of indices in aerial surveys of medium-sized mammals?

2005 ◽  
Vol 32 (3) ◽  
pp. 245 ◽  
Author(s):  
John P. Tracey ◽  
Peter J. S. Fleming ◽  
Gavin J. Melville
Keyword(s):  
Detection Probability ◽  
Sampling Period ◽  
Aerial Survey ◽  
Species Group ◽  
Probability Of Detection ◽  
Individual Species ◽  
Aerial Surveys ◽  
South Wales ◽  
Maximum Likelihood Methods ◽  
Wildlife Populations

Although aerial surveys are an effective and commonly used method of monitoring wildlife populations, variable detection probability may result in unreliable indices or biased estimates of absolute abundance. Detection probability can vary between sites, sampling periods, species, group sizes, vegetation types and observers. These variables were examined in helicopter surveys of a suite of medium-sized mammals in a hilly environment in central eastern New South Wales. Maximum-likelihood methods were used to investigate the effects of these variables on detection probability, which was derived using the double-count technique. Significant differences were evident between species in the overall analysis, and group size, vegetation, observer pair and sampling period for various individual species when analysed separately. The implications for monitoring wildlife populations between sites and across time are discussed. This paper emphasises that aerial survey indices may be effective in detecting large differences in population size but can be improved by quantifying detection probabilities for a range of variables.

Size and density of a feral goat Capra hircus population in a protected area in the Pyrenees

2013 ◽  
Vol 9 (3) ◽  
Author(s):  
Juan Herrero ◽  
Olatz Fernández ◽  
Carlos Prada ◽  
Alicia García-Serrano
Keyword(s):  
Protected Area ◽  
Capra Hircus ◽  
Feral Goat

scholarly journals Leopard Density Estimation within an Enclosed Reserve, Namibia Using Spatially Explicit Capture-Recapture Models

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 724
Author(s):  
Noack ◽  
Heyns ◽  
Rodenwoldt ◽  
Edwards
Keyword(s):  
Management Strategies ◽  
Camera Traps ◽  
Spatially Explicit ◽  
Conservation Areas ◽  
Term Survival ◽  
New Class ◽  
Capture Recapture ◽  
Wildlife Populations ◽  
Effective Conservation

The establishment of enclosed conservation areas are claimed to be the driving force for the long-term survival of wildlife populations. Whilst fencing provides an important tool in conservation, it simultaneously represents a controversial matter as it stops natural migration processes, which could ultimately lead to inbreeding, a decline in genetic diversity and local extinction if not managed correctly. Thus, wildlife residing in enclosed reserves requires effective conservation and management strategies, which are strongly reliant on robust population estimates. Here, we used camera traps combined with the relatively new class of spatially explicit capture-recaptured models (SECR) to produce the first reliable leopard population estimate for an enclosed reserve in Namibia. Leopard density was estimated at 14.51 leopards/100 km2, the highest recorded density in Namibia to date. A combination of high prey abundance, the absence of human persecution and a lack of top-down control are believed to be the main drivers of the recorded high leopard population. Our results add to the growing body of literature which suggests enclosed reserves have the potential to harbour high densities and highlight the importance of such reserves for the survival of threatened species in the future.

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.

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 On the Estimation of Dispersal and Movement of Birds

The Condor ◽  
2004 ◽  
Vol 106 (4) ◽  
pp. 720-731 ◽  
Author(s):  
William L. Kendall ◽  
James D. Nichols
Keyword(s):  
Robust Design ◽  
Nonlinear Dynamic ◽  
Detection Probability ◽  
Cross Correlation ◽  
Statistical Methodology ◽  
Open Population ◽  
Abundance Estimates ◽  
Capture Recapture ◽  
Over Time

Abstract The estimation of dispersal and movement is important to evolutionary and population ecologists, as well as to wildlife managers. We review statistical methodology available to estimate movement probabilities. We begin with cases where individual birds can be marked and their movements estimated with the use of multisite capture-recapture methods. Movements can be monitored either directly, using telemetry, or by accounting for detection probability when conventional marks are used. When one or more sites are unobservable, telemetry, band recoveries, incidental observations, a closed- or open-population robust design, or partial determinism in movements can be used to estimate movement. When individuals cannot be marked, presence-absence data can be used to model changes in occupancy over time, providing indirect inferences about movement. Where abundance estimates over time are available for multiple sites, potential coupling of their dynamics can be investigated using linear cross-correlation or nonlinear dynamic tools. Sobre la Estimación de la Dispersión y el Movimiento de las Aves Resumen. La estimación de la dispersión y el movimiento es importante para los ecó logos evolutivos y de poblaciones, así como también para los encargados del manejo de vida silvestre. Revisamos la metodología estadística disponible para estimar probabilidades de movimiento. Empezamos con casos donde aves individuales pueden ser marcadas y sus movimientos estimados con el uso de métodos de captura-repactura para múltiples sitios. Los movimientos pueden ser monitoreados ya sea directamente, usando telemetría o teniendo en cuenta las probabilidades de detección cuando se usan marcas convencionales. Cuando uno o más sitios no pueden ser observados, se puede estimar el movimiento usando telemetría, recuperación de anillos, observaciones circunstanciales, un diseño poblacional robusto cerrado o abierto, o determinismo parcial de los movimientos. Cuando los individuos no pueden ser marcados, se pueden usar datos de presencia-ausencia para modelar los cambios en el tiempo de la ocupación, brindando inferencias indirectas sobre los movimientos. Cuando las estimaciones de abundancia a lo largo del tiempo están disponibles para varios sitios, se puede investigar la interrelación potencial de sus dinámicas usando correlaciones cruzadas lineales o herramientas para dinámica no lineal.

On bias, precision and accuracy in wildlife aerial surveys

2008 ◽  
Vol 35 (4) ◽  
pp. 253 ◽  
Author(s):  
Jim Hone
Keyword(s):  
Survey Data ◽  
Wildlife Management ◽  
Aerial Survey ◽  
Review Of The Literature ◽  
Density Estimates ◽  
True Value ◽  
Wildlife Population ◽  
Aerial Surveys ◽  
Precision And Accuracy ◽  
Definition Of

Bias, precision and accuracy have been studied extensively in wildlife population estimation including aerial surveys. A review of the literature shows that the concepts of bias and precision are used broadly consistently. Aerial survey data from known populations of feral pig carcases and white-tailed deer show that few density estimates are unbiased and precise. Research is needed, however, to clarify how much bias and how much precision are enough for the various types of wildlife management activities. Accuracy is used in two closely related but different ways. One set of definitions of accuracy relates to deviations from the true value (bias) and the second set relates to squared deviations from the true value (bias and precision). The implications are that authors are encouraged to clearly state which definition of accuracy they use, or focus solely on bias and precision.

An Evaluation of Feral Goat Control Methods on Currawinya National Park, South-Western Queensland.

1997 ◽  
Vol 19 (2) ◽  
pp. 166 ◽  
Author(s):  
GP Edwards ◽  
TF Clancy ◽  
J Lee ◽  
J Mcdonnell
Keyword(s):  
National Park ◽  
Best Practice ◽  
Control Program ◽  
Aerial Survey ◽  
Accurate Estimate ◽  
Control Methods ◽  
Control Programs ◽  
Feral Animals ◽  
Initial Survey ◽  
Feral Goat

This study was designed to develop monitoring techniques based on aerial survey and to evaluate the effectiveness of control methods for large feral herbivores (especially goats) in the mulga woodlands. It was conducted on a study site in south-western Queensland encompassing Currawinya National Park. Two control programs were undertaken on the park: a ground-based shooting program and a two-stage live-muster and aerial culling program. Population estimates of feral goats based on 100 m strip counts from a fixed-wing aircraft were 4.6/km2 for the survey block for the initial survey with a decline to less than 2/km2 by the end of the project. It was concluded that fixed-wing aerial surveys provide an accurate estimate of the density of large feral herbivores, such as goats. The first control program, based on ground-based shooting, was of only limited success. The second control program, based on contract mustering with the aid of fixed-wing aircraft followed by aerial culling using a helicopter, was very successful in reducing the number of feral goats and other feral animals. This approach represents best practice management of feral goats in the mulga woodlands. The effect of the reduction in goat numbers on goat impact within the park is yet to be evaluated.

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