Detection probability in aerial surveys of feral horses

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. © 2011 The Wildlife Society.     

An Evaluation of Survey Methods for Estimating Northern Bobwhite Abundance in Southern Texas

Abstract: Distance sampling has been identified as a reliable and well-suited method for estimating northern bobwhite (Colinus virginianus) density. However, distance sampling using walked transects requires intense sampling to obtain precise estimates, thus making the technique impractical for large acreages. Researchers have addressed this limitation by either resorting to the use of indices (e.g., morning covey-call surveys) or incorporating the use of aerial surveys with distance sampling. Both approaches remain relatively untested. Our objectives were to 1) compare density estimates among morning covey-call surveys, helicopter transects, and walked transects; 2) test a critical assumption of distance sampling pertinent to helicopter surveys (i.e., all objects on line are detected); and 3) evaluate the underlying premise of morning covey-call surveys (i.e., that the no. of calling coveys correlates with bobwhite density). Our study was conducted on 3 study sites in Brooks County, Texas, USA, during October to December, 2001 to 2005. Comparisons between walked transects and morning covey-call surveys involved the entire 5-year data set, whereas helicopter transects involved only the latter 2 years. Density estimates obtained from helicopter transects were similar to walked transect estimates for both years. We documented a detection probability on the helicopter transect line of 70 ± 10.2% (% ± SE; n = 20 coveys). Morning covey-call surveys yielded similar density estimates to walked transect estimates during only 2 of 5 years, when walked transect estimates were the least accurate and precise. We detected a positive relationship (R² = 0.51; 95% CI for slope: 29.5–53.1; n = 63 observations) between covey density and number of coveys heard calling. We conclude that helicopter transects appear to be a viable alternative to walked transects for estimating density of bobwhites. Morning covey-call surveys appear to be a poor method to estimate absolute abundance and to depict general population trajectories.     

A Sightability Model for Mountain Goats

ABSTRACT Unbiased estimates of mountain goat (Oreamnos americanus) populations are key to meeting diverse harvest management and conservation objectives. We developed logistic regression models of factors influencing sightability of mountain goat groups during helicopter surveys throughout the Cascades and Olympic Ranges in western Washington during summers, 2004–2007. We conducted 205 trials of the ability of aerial survey crews to detect groups of mountain goats whose presence was known based on simultaneous direct observation from the ground (n = 84), Global Positioning System (GPS) telemetry (n = 115), or both (n = 6). Aerial survey crews detected 77% and 79% of all groups known to be present based on ground observers and GPS collars, respectively. The best models indicated that sightability of mountain goat groups was a function of the number of mountain goats in a group, presence of terrain obstruction, and extent of overstory vegetation. Aerial counts of mountain goats within groups did not differ greatly from known group sizes, indicating that under-counting bias within detected groups of mountain goats was small. We applied Horvitz-Thompson-like sightability adjustments to 1,139 groups of mountain goats observed in the Cascade and Olympic ranges, Washington, USA, from 2004 to 2007. Estimated mean sightability of individual animals was 85% but ranged 0.75–0.91 in areas with low and high sightability, respectively. Simulations of mountain goat surveys indicated that precision of population estimates adjusted for sightability biases increased with population size and number of replicate surveys, providing general guidance for the design of future surveys. Because survey conditions, group sizes, and habitat occupied by goats vary among surveys, we recommend using sightability correction methods to decrease bias in population estimates from aerial surveys of mountain goats.     

Spotlight surveys for white-tailed deer: Monitoring panacea or exercise in futility?

Many monitoring programs for white-tailed deer (Odocoileus virginianus) on both private and public lands across the United States have long relied on the use of road-based spotlight surveys for monitoring population size and trends. Research has suggested spotlight surveys are ineffective and that road-based surveys for deer are biased because of highly variable detection rates. To evaluate variability in detection rates relative to the assumption that repeated surveys along roads will provide reliable trend data for use in calculating deer density estimates, we collected 5 years of thermal-imager and spotlight survey data using a multiple-observer, closed-capture approach. Using a Huggin's closed capture model, data bootstrapping, and variance components analyses, our results suggest that density estimates for white-tailed deer generated from data collected during road-based spotlight surveys are likely not reflective of the standing deer population. Detection probabilities during individual spotlight surveys ranged from 0.00 to 0.80 (median = 0.45) across all surveys, and differed by observer, survey, management unit, and survey transect replicate. Mean spotlight detection probability (0.41) and process standard deviation (0.12) estimates indicated considerable variability across surveys, observers, transects, and years, which precludes the generation of a correction factor or use of spotlight data to evaluate long-term trends at any scale. Although recommended by many state, federal, and non-governmental agencies, our results suggest that the benefit of spotlight survey data for monitoring deer populations is limited and likely represents a waste of resources with no appreciable management information gained. © 2012 The Wildlife Society.     

Detectability of lesser prairie-chicken leks: A comparison of surveys from aircraft

Lesser prairie-chickens (Tympanuchus pallidicinctus) are traditionally monitored by spring road-based lek surveys and counts of males attending leks. Several weaknesses exist with ground-based monitoring methods such as the bias of restricting surveys to roads, unknown probability of lek detection, and man-hours required to survey large tracts of habitat. We evaluated aerial surveys to locate lesser prairie-chicken leks in Texas and New Mexico using a Cessna 172 airplane (C172), R-22 Beta II helicopter (R-22), and R-44 Raven II helicopter (R-44) during spring 2007–2008. We determined lek activity during surveys with remote cameras placed on leks and cross-referenced time on the photo frame to time on our Global Positioning System flight log. From remote cameras we found that 305 leks were available for detection during survey flights. We determined lek detectability was 32.7% (95% CI = 20.3–47.1%) in the C172, 72.3% (64.50–79.14%) in the R-22, and 89.8% (82.0–95.0%) in the R-44. We created 16 a priori logistic regression models incorporating aircraft platform, distance to lek, survey date, lek size, and lek type to explain lek detection from aerial surveys. Our top ranked model included platform, distance, and lek type (model weight; w_i = 0.288). We had four competitive models and model averaged to draw inferences. Model averaging showed that detectability was generally greatest with the R-44, followed by the R-22, and lowest with the C172, with a slight deviation from this ranking at increased distances. Within our transect width, model averaging also suggested that detectability decreased as distance from the transect to the lek increased during helicopter surveys, and detectability increased as distance from the transect to the lek increased during C172 surveys. Furthermore, man-made leks were more likely to be detected than natural leks and large leks were more likely to be detected than medium or small leks. Aerial surveys effectively locate new leks and monitor lek density, and alleviate weaknesses associated with ground-based monitoring. We recommend using the R-44 to conduct lek surveys while flying at an altitude of 15 m at a speed of 60 km/hr on sunny mornings. © 2011 The Wildlife Society.     

Estimation and Correction of Visibility Bias in Aerial Surveys of Wintering Ducks

Abstract: Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1–100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36–42%, and associated standard errors increased 38–55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):808–813; 2008)     

Mark-resight and sightability modeling of a western Washington elk population

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 (log₂) 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. © 2012 The Wildlife Society.     

Evaluating Ungulate Mortality Associated With Helicopter Net-Gun Captures in the Northern Great Plains

ABSTRACT Ungulate mortality from capture-related injuries is a recurring concern for researchers and game managers throughout North America and elsewhere. We evaluated effects of 7 variables to determine whether ungulate mortality could be reduced by modifying capture and handling procedures during helicopter net-gunning. During winter 2001–2006, we captured 208 white-tailed deer (Odocoileus virginianus) and 281 pronghorn (Antilocapra Americana) by helicopter net-gunning throughout the Northern Great Plains. Of 281 pronghorn, 25 (8.9%) died from capture-related injuries; 12 were from direct injuries during capture, and 13 occurred postrelease. Of 208 deer, 3 (1.4%) died from injuries sustained during helicopter captures, with no mortalities documented postrelease. We used logistic regression to evaluate the probability that ungulates would die of injuries associated with helicopter net-gun captures by analyzing effects of snow depth, transport distance, ambient and rectal temperatures, pursuit and handling times, and whether individuals were transported to processing sites. The probability of capture-related mortality postrelease decreased 58% when transport distance was reduced from 14.5 km to 0 km and by 69% when pursuit time decreased from 9 minutes to <1 minute. Wildlife managers and researchers using helicopter capture services in landscapes of the Midwest should limit pursuit time and eliminate animal transport during pronghorn and white-tailed deer capture operations to minimize mortality rates postrelease.     

AnimalFinder: A semi-automated system for animal detection in time-lapse camera trap images

Although the use of camera traps in wildlife management is well established, technologies to automate image processing have been much slower in development, despite their potential to drastically reduce personnel time and cost required to review photos. We developed AnimalFinder in MATLAB® to identify animal presence in time-lapse camera trap images by comparing individual photos to all images contained within the subset of images (i.e. photos from the same survey and site), with some manual processing required to remove false positives and collect other relevant data (species, sex, etc.). We tested AnimalFinder on a set of camera trap images and compared the presence/absence results with manual-only review with white-tailed deer (Odocoileus virginianus), wild pigs (Sus scrofa), and raccoons (Procyon lotor). We compared abundance estimates, model rankings, and coefficient estimates of detection and abundance for white-tailed deer using N-mixture models. AnimalFinder performance varied depending on a threshold value that affects program sensitivity to frequently occurring pixels in a series of images. Higher threshold values led to fewer false negatives (missed deer images) but increased manual processing time, but even at the highest threshold value, the program reduced the images requiring manual review by ~ 40% and correctly identified > 90% of deer, raccoon, and wild pig images. Estimates of white-tailed deer were similar between AnimalFinder and the manual-only method (~ 1–2 deer difference, depending on the model), as were model rankings and coefficient estimates. Our results show that the program significantly reduced data processing time and may increase efficiency of camera trapping surveys.     

Incorporating Estimates of Group Size in Sightability Models for Wildlife

ABSTRACT Sightability models have been used to estimate population size of many wildlife species; however, a limitation of these models is an assumption that groups of animals observed and counted during aerial surveys are enumerated completely. Replacing these unknown counts with maximum observed counts, as is typically done, produces population size estimates that are negatively biased. This bias can be substantial depending on the degree of undercounting occurring. We first investigated a method-of-moments estimator of group sizes. We then defined a population size estimator using the method-of-moments estimator of group sizes in place of maximum counts in the traditional sightability models, thereby correcting for bias associated with undercounting group size. We also provide associated equations for calculating the variance of our estimator. This estimator is an improvement over existing sightability model techniques because it significantly reduces bias, and variance estimates provide near nominal confidence interval coverage. The data needed for this estimator can be easily collected and implemented by wildlife managers with a field crew of only 3 individuals and little additional flight or personnel time beyond the normal requirements for developing sightability models.     

Blind Count Surveys of White-Tailed Deer and Population Estimates Using Bowden's Estimators

Abstract: Conducting surveys from blinds when supplemental feed (bait) has been provided has not been evaluated for estimating parameters of ungulate populations. We conducted blind count surveys of white-tailed deer (Odocoileus virginianus) in a 214-ha enclosure in central Texas, USA, in 2007 and 2008 to address 2 main objectives: 1) to evaluate a blind count survey protocol developed for use on small parcels of land, and 2) to use data collected from blind count surveys to conduct simulations to evaluate the reliability of abundance and sex ratio estimates obtained from Bowden's estimator. In each year population abundance (2007: 60; 2008: 48) and sex ratio (M:F, 2007: 0.58; 2008: 0.71) were known as were sighting frequencies of every animal. The enclosure had 5 blinds and we baited each blind with corn. We encountered many deer during surveys because there were only 2 deer in 2007 and 1 deer in 2008 that we did not view from blinds ≥1 time. To evaluate bias and precision of abundance and sex ratio estimates we conducted 10,000 bootstrap simulations. We evaluated both parameters in relation to the percentage of each population marked, number of surveys conducted from blinds, and whether surveys were conducted in the morning, evening, or both morning and evening. Also, we evaluated abundance in relation to whether we identified animals with unique marks to individual, and we evaluated sex ratio in relation to intersexual distribution of marks. Abundance estimates were less biased and more precise when we uniquely identified all marked animals and 40–70% of the population was marked. Sex ratio estimates were less biased when 40–70% of the population was marked and surveys were conducted in the morning and evening. Sex ratio estimates, however, were less precise than abundance estimates. Unbiased estimates of white-tailed deer population parameters can be obtained from blind count surveys conducted on small parcels of enclosed land and when animals are baited.     

Comparison of Effects of Shed Antler Hunting and Helicopter Surveys on Ungulate Movements and Space Use

Shed antler hunting (i.e., collecting cast cervid antlers) has increased in popularity during the past decade, but little is known about how this recreational activity affects ungulate movements and space use. We placed geographic positioning system (GPS)-collars on 133 female and male bighorn sheep (Ovis canadensis), bison (Bison bison), and mule deer (Odocoileus hemionus) to quantify their movements and space use during shed antler hunts compared with those behaviors during helicopter surveys in Utah, USA, from 2012 to 2015. For each species, we calculated means and 95% confidence intervals for distance moved during 90-minute segments (16 points/day) pre-event (control, 7 consecutive days prior to event), event (1–2 days), and post-event (7 consecutive days after event) for shed antler hunts and helicopter surveys. We also compared use of space for each species during these events. Female bighorn sheep did not increase distance moved or substantially change space use during shed antler hunts and helicopter surveys. Male bighorn sheep increased distance moved 41% on average during shed antler hunts and by 2.02 times during helicopter surveys but did not change space use during those events. Female bison increased distance moved 15% on average during shed antler hunts and 30% during helicopter surveys. Mule deer increased distance moved and altered space use the most during shed antler hunts; females increased distance moved 97%, and 54% of females moved a mean distance of 742 ± 642 (SD) m (range = 9–3,778 m) outside of their home ranges during those hunts for a mean of 9.2 ± 9.4 hours (range = 1.5 to 41 hr). Male mule deer increased distance moved by 2.10 times on average during shed antler hunts, and 82% of males moved a mean distance of 1,264 ± 732 m (range = 131–3,637 m) outside of their home ranges during those hunts for a mean of 12.6 ± 7.6 hours (range = 4.5–33 hr). Our results provide timely information about how legal shed antler hunting affects movements and space use of female and male ungulates, especially mule deer, and can guide the conservation of ungulate populations and their habitat. © 2021 The Wildlife Society.     

Effects of ambient noise on detectability and localization of avian songs and tones by observers in grasslands

Probability of detection and accuracy of distance estimates in aural avian surveys may be affected by the presence of anthropogenic noise, and this may lead to inaccurate evaluations of the effects of noisy infrastructure on wildlife. We used arrays of speakers broadcasting recordings of grassland bird songs and pure tones to assess the probability of detection, and localization accuracy, by observers at sites with and without noisy oil and gas infrastructure in south‐central Alberta from 2012 to 2014. Probability of detection varied with species and with speaker distance from transect line, but there were few effects of noisy infrastructure. Accuracy of distance estimates for songs and tones decreased as distance to observer increased, and distance estimation error was higher for tones at sites with infrastructure noise. Our results suggest that quiet to moderately loud anthropogenic noise may not mask detection of bird songs; however, errors in distance estimates during aural surveys may lead to inaccurate estimates of avian densities calculated using distance sampling. We recommend caution when applying distance sampling if most birds are unseen, and where ambient noise varies among treatments.     

Bias in manta tow surveys of Acanthaster planci

To investigate the biases associated with manta tow surveys of Acanthaster planci, counts obtained by manta-towed observers were compared with counts made on SCUBA swims under a limited range of conditions. Five 10 m wide strip transects on different parts of two reefs and with different densities of A. planci were surveyed. On average, 22.7% of starfish counted on SCUBA searches were counted on manta tows over the same transect (SD=12.0%, n=69). This proportion is termed sightability. As sightability decreases with increasing transect width, we estimate that, on average, less than 5% of the A. planci present are counted on routine manta tows which are conducted over transect of undefined width. Multiple regression analysis was used to determine which of 33 variables explained most of the variation in sightability in 10 m wide transects. The most influential variables were the proportion of cryptic starfish and an index of the degree of reef complexity. A regression equation designed to improve estimates of the abundance of A. planci on routine manta tows was developed. However, as it explained only 39% of the variation in sightability, this equation is of limited value in stabilising the negative bias associated with manta tow counts. In view of the variability of this bias, the manta tow technique is not suitable for estimating absolute densities of A. planci. Manta tow surveys are appropriate for identifying gross relative differences between densities of A. planci, and thus for determining broad-scale patterns of abundance. As such, they are a cost-effective method of estimating the geographical extent of A. planci outbreaks.     

Overabundance of Black-Tailed Deer in Urbanized Coastal California

Abundance of mule deer (Odocoileus hemionus) in western North America is often considered lower than desirable for hunting. Some coastal populations of Columbian black-tailed deer (O. h. columbianus) in California, USA, near urban development, however, are perceived as a nuisance and may be overabundant. To determine the density of a potential nuisance population in Marin County, California, we used a combination of fecal DNA surveys, camera stations, and 2 sources of ancillary data on wildlife observations. We estimated an average density of 18.3 deer/km² (90% CI = 15.8–20.7) throughout Marin County during late summer and early fall, 2015 and 2016. Within the county, areas with intermediate human density (885 people/km², 90% CI = 125–1,646) were associated with the highest deer densities (25–44/km²). Our estimate of average deer density was 1.7–6.1 times higher than published density estimates for deer from elsewhere in California and on the low end of densities reported for mule and white-tailed (O. virginianus) deer in regions where they routinely cause a nuisance to humans. High black-tailed deer densities in Marin County may be partially attributed to a paucity of large predators, but more investigation is warranted to evaluate the effects of a recent increase in coyotes (Canis latrans) on the deer population. Analyses of highway road kill rates and citizen science surveys suggest that the deer population in Marin County has been stable over the past 10 years. Our results demonstrate how robust estimation of deer density can inform human–wildlife conflict issues, not just managed hunting. © 2020 The Wildlife Society.     

Detection Probability and Sources of Variation in White-Tailed Deer Spotlight Surveys

Abstract: As a first step in understanding structure and dynamics of white-tailed deer (Odocoileus virginianus) populations, managers require knowledge of population size. Spotlight counts are widely used to index deer abundance; however, detection probabilities using spotlights have not been formally estimated. Using a closed mark—recapture design, we explored the efficiency of spotlights for detecting deer by operating thermal imagers and spotlights simultaneously. Spotlights detected only 50.6% of the deer detected by thermal imagers. Relative to the thermal imager, spotlights failed to detect 44.2% of deer groups (≥1 deer). Detection probabilities for spotlight observers varied between and within observers, ranging from 0.30 (SE = 0.053) to 0.66 (SE = 0.058). Managers commonly assume that although road counts based on convenience sampling designs are imperfect, observers can gather population-trend information from repeated counts along the same survey route. Our results indicate detection rate varied between and within observers and surveyed transects. If detection probabilities are substantially affected by many variables, and if transect selection is not based on appropriate sampling designs, it may be impractical to correct road spotlight counts for detection probabilities to garner unbiased estimates of population size.     

Feral donkey Equus asinus populations on the Karpaz peninsula, Cyprus

Numerous researchers have documented the adverse effects of feral donkeys Equus asinus introduced to semi-arid ecosystems. With the release of feral donkeys and potential increasing populations in natural habitats in northern Cyprus, there is concern for negative impacts on vegetation and native species. In the north of the island, there has been only one published study of feral donkey populations, and population estimators were relatively subjective. We estimated feral donkey populations on the Karpaz Peninsula using line transect surveys and quantitative distance sampling estimators. We stratified the sampling by using 11 sample units within the study area. We evaluated potential biases associated with habitat, topography, and perpendicular distance from the transect line and found that these variables did not bias donkey detections during our surveys. Using program DISTANCE, we found that a hazard rate cosine model was the best model that described our distance data based on model selection criterion (Akaikes Information Criteria adjusted for small sample bias). Estimated effective strip width was 280.19 m and detection probability was 0.47 with this model. Estimated donkey density was 6.7 donkeys/km², and estimated total abundance was 800 donkeys for the entire 132.5 km² study area. Of 95 donkey groups detected: 16% were detected in agricultural habitats with flat topography, 9% were detected in agricultural habitats with sloped topography, 24% were detected in shrub/forest habitats with flat topography, and 51% were detected in shrub/forest habitats with sloped topography. Of 102 behavioral observations recorded (multiple behaviors were detected in groups), frequencies of behaviors were 1% bedded, 70% standing, 22% grazing, 6% moving, and 2% other. Our estimated donkey population density in the Karpaz Peninsula was >2 times densities reported in arid regions of the United States and Australia, but slightly lower than earlier density estimates reported for the Karpaz region. These estimates of feral donkey populations in the Karpaz Peninsula provide a quantitative baseline from which to make population management decisions.     

PROBLEMS WITH SAMPLING DESERT TORTOISES: A SIMULATION ANALYSIS BASED ON FIELD DATA

Abstract: The desert tortoise (Gopherus agassizii) was listed as a U.S. threatened species in 1990 based largely on population declines inferred from mark-recapture surveys of 2.59-km² (1-mi²) plots. Since then, several census methods have been proposed and tested, but all methods still pose logistical or statistical difficulties. We conducted computer simulations using actual tortoise location data from 2 1-mi² plot surveys in southern California, USA, to identify strengths and weaknesses of current sampling strategies. We considered tortoise population estimates based on these plots as “truth” and then tested various sampling methods based on sampling smaller plots or transect lines passing through the mile squares. Data were analyzed using Schnabel's mark-recapture estimate and program CAPTURE. Experimental subsampling with replacement of the 1-mi² data using 1-km² and 0.25-km² plot boundaries produced data sets of smaller plot sizes, which we compared to estimates from the 1-mi² plots. We also tested distance sampling by saturating a 1-mi² site with computer simulated transect lines, once again evaluating bias in density estimates. Subsampling estimates from 1-km² plots did not differ significantly from the estimates derived at 1-mi². The 0.25-km² subsamples significantly overestimated population sizes, chiefly because too few recaptures were made. Distance sampling simulations were biased 80% of the time and had high coefficient of variation to density ratios. Furthermore, a prospective power analysis suggested limited ability to detect population declines as high as 50%. We concluded that poor performance and bias of both sampling procedures was driven by insufficient sample size, suggesting that all efforts must be directed to increasing numbers found in order to produce reliable results. Our results suggest that present methods may not be capable of accurately estimating desert tortoise populations.     

Challenges and opportunities for estimating abundance of a low-density moose population

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low-density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance-sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km²) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280-km² Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.     

Using spotlight observations to predict resource selection and abundance for white-tailed deer

Spotlight surveys for white-tailed deer (Odocoileus virginianus) can yield large presence-only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat-based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence-only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county-level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat-based density estimator (extrapolation) and zero-inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence-only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.