Wildlife Abundance and Diversity as Indicators of Tourism Potential in Northern Botswana

Wildlife tourism can provide economic incentives for conservation. Due to the abundance of wildlife and the presence of charismatic species some areas are better suited to wildlife tourism. Our first objective was to develop criteria based on wildlife abundance and diversity to evaluate tourism potential in the Northern Conservation Zone of Botswana. Secondly we wanted to quantify and compare tourism experiences in areas with high and low tourism potential. We used aerial survey data to estimate wildlife biomass and diversity to determine tourism potential, while data from ground surveys quantified the tourist experience. Areas used for High Paying Low Volume tourism had significantly higher mean wildlife biomass and wildlife diversity than the areas avoided for this type of tourism. Only 22% of the Northern Conservation Zone has intermediate to high tourism potential. The areas with high tourism potential, as determined from the aerial survey data, provided tourists with significantly better wildlife sightings (ground surveys) than the low tourism potential areas. Even Low Paying tourism may not be economically viable in concessions that lack areas with intermediate to high tourism potential. The largest part of the Northern Conservation Zone has low tourism potential, but low tourism potential is not equal to low conservation value. Alternative conservation strategies should be developed to complement the economic incentive provided by wildlife-based tourism in Botswana.     

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)     

Using Web images to train a deep neural network to detect sparsely distributed wildlife in large volumes of remotely sensed imagery: A case study of polar bears on sea ice

Remote sensing can be a valuable alternative or complement to traditional techniques for monitoring wildlife populations, but often entails operational bottlenecks at the image analysis stage. For example, photographic aerial surveys have several advantages over surveys employing airborne observers or other more intrusive monitoring techniques, but produce onerous amounts of imagery for manual analysis when conducted across vast areas, such as the Arctic. Deep learning algorithms, chiefly convolutional neural networks (CNNs), have shown promise for automatically detecting wildlife in large and/or complex image sets. But for sparsely distributed species, such as polar bears (Ursus maritimus), there may not be sufficient known instances of the animals in an image set to train a CNN. We investigated the feasibility of instead providing ‘synthesized’ training data to a CNN to detect polar bears throughout large volumes of aerial imagery from a survey of the Baffin Bay subpopulation. We harvested 534 miscellaneous images of polar bears from the Web that we edited to more closely resemble 21 known images of bears from the aerial survey that were solely used for validation. We combined the Web images of polar bears with 6292 random background images from the aerial survey to train a CNN (ResNet-50), which subsequently correctly classified 20/21 (95%) bear images from the survey and 1172/1179 (99.4%) random background validation images. Given that even a small background misclassification rate could produce multitudinous false positives over many thousands of photos, we describe a potential workflow to efficiently screen out erroneous detections. We also discuss potential avenues to improve CNN accuracy, and the broader applicability of our approach to other image-based wildlife monitoring scenarios. Our results demonstrate the feasibility of using miscellaneously sourced images of animals to train deep neural networks for specific wildlife detection tasks.     

Cost and Precision Functions for Aerial Quadrat Surveys: a Case Study of Ring-Necked Ducks in Minnesota

ABSTRACT Cost considerations may be as important as precision when making survey-design choices, and the ability to accurately estimate survey costs will be essential if survey budgets become more constrained. We used data from a survey of ring-necked ducks (Aythya collaris) to illustrate how simple distance formulas can be used to construct a cost function for aerial quadrat surveys. Our cost function provided reasonable estimates of effort (hr) and costs, and allowed us to evaluate plot-size choices in terms of expected cost-precision tradeoffs. Although factors influencing costs in wildlife surveys can be complicated, we believe that cost functions deserve more attention and should be routinely considered in conjunction with traditional power analyses.     

Bias in aerial estimates of the number of nests in White Ibis and Great Egret colonies

ABSTRACT Estimating detection error, as well as the magnitude of other potential survey biases, is essential when sampling efforts play a role in the estimation of population size and management of wildlife populations. We quantified visual biases in aerial surveys of nesting wading birds (Ciconiiformes) in colonies in the Florida Everglades using a negative binomial count regression model to compare numbers of nests in quadrats counted on the ground with numbers estimated from aerial photographs of the same quadrats. The model also allowed the determination of degree of difference between monitoring results based upon such factors as nest density, vegetative cover, and nest turnover rates. Aerial surveys of White Ibis (Eudocimus albus) colonies underestimated the true number of nests found during ground counts by 11.1%, and underestimates were significantly greater (P= 0.047) in a colony with high nest turnover. Error rates did not differ for quadrats that varied in the density of White Ibis nests did not differ, and visual bias did not increase with vegetative complexity (P= 0.73). Estimates of nest density in colonies of Great Egrets (Ardea alba) based on aerial surveys were higher than ground counts for 38% of the quadrats sampled, and mean visual bias was 23.1%. Species misidentification likely contributed to visibility bias for Great Egrets in our study, with some Snowy Egrets almost certainly mistaken for Great Egrets in aerial photos. Biases of the magnitude we observed fro Great Egrets and White Ibises can mask true population trends in long‐term monitoring and, therefore, we recommend that detection probability be explicitly evaluated when conducting aerial surveys of nesting birds.     

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.     

Validation of a landscape‐scale planning tool for cavity‐dependent wildlife

Tree cavities provide important habitat for wildlife. Effective landscape‐scale management of cavity‐dependent wildlife requires an understanding of where cavities occur, but tree cavities can be cryptic and difficult to survey. We assessed whether a landscape‐scale map of mature forest habitat availability, derived from aerial photographs, reflected the relative availability of mature trees and tree cavities. We assessed cavities for their suitability for use by wildlife, and whether the map reflected the availability of such cavities. There were significant differences between map categories in several characteristics of mature trees that can be used to predict cavity abundance (i.e. tree form and diameter at breast height). There were significant differences between map categories in the number of potential cavity bearing trees and potential cavities per tree. However, the index of cavity abundance based on observations made from the ground provided an overestimate of true cavity availability. By climbing a sample of mature trees we showed that only 5.1% of potential tree cavities detected from the ground were suitable for wildlife, and these were found in only 12.5% of the trees sampled. We conclude that management tools developed from remotely sensed data can be useful to guide decision‐making in the conservation management of tree cavities but stress that the errors inherent in these data limit the scale at which such tools can be applied. The rarity of tree cavities suitable for wildlife in our study highlights the need to conserve the tree cavity resource across the landscape, but also the importance of increasing the accuracy of management tools for decision‐making at different scales. Mapping mature forest habitat availability at the landscape scale is a useful first step in managing habitat for cavity‐dependent wildlife, but the potential for overestimating actual cavity abundance in a particular area highlights the need for complementary on‐ground surveys.     

Identifying error and accurately interpreting environmental DNA metabarcoding results: A case study to detect vertebrates at arid zone waterholes

Environmental DNA (eDNA) metabarcoding surveys enable rapid, noninvasive identification of taxa from trace samples with wide‐ranging applications from characterizing local biodiversity to identifying food‐web interactions. However, the technique is prone to error from two major sources: (a) contamination through foreign DNA entering the workflow, and (b) misidentification of DNA within the workflow. Both types of error have the potential to obscure true taxon presence or to increase taxonomic richness by incorrectly identifying taxa as present at sample sites, but multiple error sources can remain unaccounted for in metabarcoding studies. Here, we use data from an eDNA metabarcoding study designed to detect vertebrate species at waterholes in Australia's arid zone to illustrate where and how in the workflow errors can arise, and how to mitigate those errors. We detected the DNA of 36 taxa spanning 34 families, 19 orders and five vertebrate classes in water samples from waterholes, demonstrating the potential for eDNA metabarcoding surveys to provide rapid, noninvasive detection in remote locations, and to widely sample taxonomic diversity from aquatic through to terrestrial taxa. However, we initially identified 152 taxa in the samples, meaning there were many false positive detections. We identified the sources of these errors, allowing us to design a stepwise process to detect and remove error, and provide a template to minimize similar errors that are likely to arise in other metabarcoding studies. Our findings suggest eDNA metabarcoding surveys need to be carefully conducted and screened for errors to ensure their accuracy.     

Determining Occurrence Dynamics when False Positives Occur: Estimating the Range Dynamics of Wolves from Public Survey Data

Large-scale presence-absence monitoring programs have great promise for many conservation applications. Their value can be limited by potential incorrect inferences owing to observational errors, especially when data are collected by the public. To combat this, previous analytical methods have focused on addressing non-detection from public survey data. Misclassification errors have received less attention but are also likely to be a common component of public surveys, as well as many other data types. We derive estimators for dynamic occupancy parameters (extinction and colonization), focusing on the case where certainty can be assumed for a subset of detections. We demonstrate how to simultaneously account for non-detection (false negatives) and misclassification (false positives) when estimating occurrence parameters for gray wolves in northern Montana from 2007–2010. Our primary data source for the analysis was observations by deer and elk hunters, reported as part of the state’s annual hunter survey. This data was supplemented with data from known locations of radio-collared wolves. We found that occupancy was relatively stable during the years of the study and wolves were largely restricted to the highest quality habitats in the study area. Transitions in the occupancy status of sites were rare, as occupied sites almost always remained occupied and unoccupied sites remained unoccupied. Failing to account for false positives led to over estimation of both the area inhabited by wolves and the frequency of turnover. The ability to properly account for both false negatives and false positives is an important step to improve inferences for conservation from large-scale public surveys. The approach we propose will improve our understanding of the status of wolf populations and is relevant to many other data types where false positives are a component of observations.     

Aerial Surveys for Estimating Wild Turkey Abundance in the Texas Rolling Plains

Abstract: Aerial surveys have been used to estimate abundance of several wild bird species including wild turkeys (Meleagris gallopavo). We used inflatable turkey decoys at 3 study sites in the Texas Rolling Plains to simulate Rio Grande wild turkey (M. g. intermedia) flocks. We evaluated detectability of flocks and errors in counting flock size during fixed-wing (Cessna 172) aerial surveys using logistic and linear regression models. Flock detectability was primarily influenced by flock size and vegetative cover, and errors in counting flock size were primarily influenced by size of flocks. We conducted computer simulations to evaluate the accuracy and precision of fixed-wing aerial surveys and examined power to detect trends in population change. Our simulations suggested abundance estimates from fixed-wing aerial surveys may be underestimated by 10-15% (2.0-4.8% CV). Power analyses suggested that fixed-wing aerial surveys can provide sufficient power (>0.80) to detect a population change of 10-25% over a 4-5-year period. We concluded fixed-wing aerial surveys are feasible on ecoregion scales.     

Quantitative assessment of species identification in aerial transect surveys for ice‐associated seals

Technological advances have facilitated collection of vast quantities of photographic data from aerial surveys of marine mammals. However, when it is difficult to distinguish species from a distance, reliable identification from aerial images can often be challenging. This is the case for ice‐associated seals, species for which global climate change has motivated intensive monitoring efforts in recent years. We assess species and age class identification from aerial images of four ice seal species (bearded seals, Erignathus barbatus; ribbon seals, Histriophoca fasciata; ringed seals, Pusa hispida; spotted seals, Phoca largha) in the Bering Sea. We also investigate the specific phenomenological and behavioral traits commonly associated with species identification and observer confidence. We generally found species and age class misidentification occurred at relatively low levels, but only 83% of spotted seals tended to be correctly identified (with 11% mistaken as ribbon seals). We also found certain traits were strong predictors for observed species, age class, or observer confidence. Our findings add to the growing body of evidence that species misidentification is pervasive in passive sampling of animal populations. Even low levels of misidentification have been demonstrated to induce substantial biases in estimators of species distribution and abundance, and it is important that statistical models account for such errors.     

Seismic Survey Design and Effects on Maternal Polar Bear Dens

Large-scale industrial activities can have negative effects on wildlife populations. Some of these effects, however, could be reduced with effective planning prior to development. The Coastal Plain of the Arctic National Wildlife Refuge, in northeastern Alaska, USA, is an important maternal denning area for polar bears (Ursus maritimus). Recent legislation has opened the area for potential oil and gas development. As a result, there is interest in conducting winter seismic surveys across the area that could disturb denning female polar bears and lead to decreased cub survival. We sought to demonstrate how different seismic survey designs, with and without aerial den detection surveys, could affect the level of potential effect on denning polar bears during spring (Feb–Apr). We developed 5 hypothetical seismic survey designs for a portion of the Coastal Plain ranging from no spatial or temporal restrictions on activities to explicit consideration of when and where operations can occur. We evaluated how many dens might be disturbed by seismic surveys and the average distance activity came within simulated polar bear dens. Survey design had a large effect on the estimated number of dens that could be disturbed; the scenario with the highest spatial and temporal specificity reduced the number of dens disturbed by >90% compared to the scenario with no restrictions on when and where activity could occur. The use of an aerial den detection survey prior to seismic activity further reduced the number of dens disturbed by 68% across all scenarios. The scenario with the highest spatial and temporal specificity always had the lowest level of disturbance for all scenarios with and without the aerial survey included. Our study suggests that large reductions in the probability of disturbance can occur through careful planning on the timing and distribution of proposed activities even when surveys are planned in areas with a high density of polar bear dens. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.     

Performance of ground-based and aerial survey methods for monitoring wildlife assemblages in a conservation area of northern Tanzania

Validating and improving field-sampling techniques for estimating wildlife community composition and population size is essential for wildlife management and conservation. We conducted ground distance sampling surveys along line transects and block counts from a small aircraft in Manyara Ranch in Northern Tanzania and contrasted estimates of species richness and species-specific densities from both sampling techniques. We used regression analyses (logistic regression and generalized linear mixed models) and model selection to investigate whether a species’ body size, group size, body color, as well as vegetation cover explained the variation in species presence/absence and relative density differences in aerial vs. ground-based sampling. Ground surveys detected significantly more species than aerial surveys. However, aerial surveys detected three species that were missed by ground surveys (African lions, African buffalo, and spotted hyena). Model selection suggested that species with smaller body mass and small group sizes were more likely to be missed in aerial surveys. Densities estimated from the aerial surveys were generally but non-significantly lower than the densities estimated from the ground surveys, with the exception of density estimates for African elephants which were slightly higher from aerial surveys. Density differences between the two methods were greater for species with small group size, light body color, and in areas with denser vegetation cover; these variables explained 75% of the variation in density differences between the two survey methods. Albeit being similar in operational costs in our relatively small study area, ground surveys yielded (1) more complete information with respect to wildlife community composition and (2) density estimates were mostly higher and (3) more precise and (4) appear more feasible to be implemented in community-based conservation schemes.     

Assessing the effectiveness of foraging radius models for seabird distributions using biotelemetry and survey data

Relatively simple foraging radius models have the potential to generate predictive distributions for a large number of species rapidly, thus providing a cost-effective alternative to large-scale surveys or complex modelling approaches. Their effectiveness, however, remains largely untested. Here we compare foraging radius distribution models for all breeding seabirds in Ireland, to distributions of empirical data collected from tracking studies and aerial surveys. At the local/colony level, we compared foraging radius distributions to GPS tracking data from seabirds with short (Atlantic puffin Fratercula arctica, and razorbill Alca torda) and long (Manx shearwater Puffinus puffinus, and European storm-petrel Hydrobates pelagicus) foraging ranges. At the regional/national level, we compared foraging radius distributions to extensive aerial surveys conducted over a two-year period. Foraging radius distributions were significantly positively correlated with tracking data for all species except Manx shearwater. Correlations between foraging radius distributions and aerial survey data were also significant, but generally weaker than those for tracking data. Correlations between foraging radius distributions and aerial survey data were benchmarked against generalised additive models (GAMs) of the aerial survey data that included a range of environmental covariates. While GAM distributions had slightly higher correlations with aerial survey data, the results highlight that the foraging radius approach can be a useful and pragmatic approach for assessing breeding distributions for many seabird species. The approach is likely to have acceptable utility in complex, temporally variable ecosystems and when logistic and financial resources are limited.     

Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non‐detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false‐positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false‐positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.     

Satellite Telemetry in Avian Research and Management: Sample Size Considerations

Abstract: Satellite tracking is currently used to make inferences to avian populations. Cost of transmitters and logistical challenges of working with some species can limit sample size and strength of inferences. Therefore, careful study design including consideration of sample size is important. We used simulations to examine how sample size, population size, and population variance affected probability of making reliable inferences from a sample and the precision of estimates of population parameters. For populations of >100 individuals, a sample >20 birds was needed to make reliable inferences about questions with simple outcomes (i.e., 2 possible outcomes). Sample size demands increased rapidly for more complex problems. For example, in a problem with 3 outcomes, a sample of >75 individuals will be needed for proper inference to the population. Combining data from satellite telemetry studies with data from surveys or other types of sampling may improve inference strength.     

Comparing data of different survey methods for sustainable wildlife management in hunting areas: the case of Tarangire–Manyara ecosystem,northern Tanzania

Cost–benefit considerations of wildlife monitoring are essential, particularly, in areas outside national park boundaries, where resources for conducting wildlife censuses are scarce, but that, at the same time, are subject to high pressure for wildlife utilization, such as hunting. Large mammal survey data from various sources were collated and analyzed to investigate which methods are best suited for monitoring purposes at low cost in the Tarangire–Manyara ecosystem, northern Tanzania. Our results indicate that primary data (from aerial and road transects counts) that involve direct species observations, although sometimes very expensive, are required for establishing the status of the target species in terms of density or population size. Concomitantly, secondary data from various sources, such as interviews, hunting quota, and damage reports, obtained over wide areas and over longer periods of time, can provide important information on presence/absence and distribution of species within an area. In addition, the study revealed that hunting quotas set did not correlate with species abundance/numbers from the primary data surveys for most of the large mammals hunted within the ecosystem. For a better conservation and management of wildlife, in particular with respect to the forthcoming formation of Wildlife Management Areas, we propose an integrated approach to wildlife monitoring using primary and secondary data sources through the involvement of local people’s knowledge.     

中国野生动物调查存在的问题与改进建议

一百余年前,人类就开始了相对系统的野生动物调查。目前已经建立了成熟的方法体系,并制定了相应的调查规范。最近几十年来,我国科研人员进行了大量的野生动物调查。但是,当前我国的野外调查规范还不够细致,调查者在调查时缺乏必要的约束,导致调查数据不规范、不可靠,很多重要信息缺失。突出问题有：样线信息不全,只有起点和终点的经纬度,没有自动记录的详细样线信息（如每秒记录一次的连续点位信息,含经纬度和时间）;动物位点信息缺乏可信度指标（如距观测者的距离）;调查时间不合理;调查地点的空间取样不均衡;记录的标准化不够（如每个观测点的观测时长不确定）等等。对此,我们参考国际通用的调查规范,提出了一些简单易行的调查要点,以便提高野外调查数据的质量。另外,我们提倡野外记录的无纸化,充分利用现有的手机应用软件（APP）和模型工具提高野外记录以及后期数据处理的效率。最后,我们提议建立固定的中国生物多样性监测样线体系,以便消除每年调查时空间取样的不确定性,更好地量化野生动物的时间动态,为野生动物的保护和管理提供依据。