云南思茅亚洲象对栖息地的选择与利用

以思茅市境内栖居的一个由5 头雌象(3 头成年体, 1 头亚成体, 1 头幼体) 组成的象群为研究对象, 利用痕迹追踪、样线调查和村庄调查等方法对野生亚洲象的栖息地选择和行为进行了初步研究。在栖息地中共记录到野生高等植物457 种, 组成5 种植被类型。旱季象群的活动区域面积是35.67 km² , 该活动区域中有3 个核心活动区, 面积分别是3.65 km² , 2.79 km² 和3.29 km² , 象群对此3 个活动核心区域循环利用; 在旱季亚洲象经常取食19种野生植物, 其中主要取食的有8 种, 还取食7 种农作物, 对农作物有一定依赖性, 这种依赖的程度随着农田周围森林中野生食物丰富程度的增加而降低; 在季雨林中亚洲象主要取食的植物种类最丰富。雨季象群的活动区域面积是18.42 km² , 该区域中只有1 个核心活动区, 面积9.08 km² 。雨季亚洲象只取食5 种野生植物, 另取食5 种农作物, 农作物为其雨季主要食物。研究结果表明, 思茅地区亚洲象栖息地中的野生食物可能不足, 人类活动对亚洲象的生存干扰较为严重, 亚洲象的行为表现出对栖息地内食物条件和人类干扰的适应。建议尽快遏止日益严重的栖息地片断化趋势, 恢复自然植被并为亚洲象提供充足的野生食物是保护该物种的关键。     

黑龙江省乌裕尔河流域丹顶鹤等珍稀涉禽航空调查报告

利用Y-11型单翼轻型飞机,航高100米,航速140公里/小时,采用且测全区调查方法,对黑龙江省乌裕尔河流域丹顶鹤等涉禽进行了航空调查,续航里程2184公里,调查面积共计为209,000公顷。结果发现调查区内有丹顶鹤(Orus japoncnsis)173只,白枕鹤(Grus vipio)19只,白鹤(Grus leucogeraous)24只,白鹳(Ciconia ciconia)19只等。     

Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys

Unmanned aircraft systems (UAS) are relatively new technologies gaining popularity among wildlife biologists. As with any new tool in wildlife science, operating protocols must be developed through rigorous protocol testing. Few studies have been conducted that quantify the impacts UAS may have on unhabituated individuals in the wild using standard aerial survey protocols. We evaluated impacts of unmanned surveys by measuring UAS‐induced behavioral responses during the nesting phase of lesser snow geese (Anser caerulescens caerulescens) in Wapusk National Park, Manitoba, Canada. We conducted surveys with a fixed‐wing Trimble UX5 and monitored behavioral changes via discreet surveillance cameras at 25 nests. Days with UAS surveys resulted in decreased resting and increased nest maintenance, low scanning, high scanning, head‐cocking and off‐nest behaviors when compared to days without UAS surveys. In the group of birds flown over, head‐cocking for overhead vigilance was rarely seen prior to launch or after landing (mean estimates 0.03% and 0.02%, respectively) but increased to 0.56% of the time when the aircraft was flying overhead suggesting that birds were able to detect the aircraft during flight. Neither UAS survey altitude nor launch distance alone in this study was strong predictors of nesting behaviors, although our flight altitudes (≥75 m above ground level) were much higher than previously published behavioral studies. Synthesis and applications: The diversity of UAS models makes generalizations on behavioral impacts difficult, and we caution that researchers should design UAS studies with knowledge that some minimal disturbance is likely to occur. We recommend flight designs take potential behavioral impacts into account by increasing survey altitude where data quality requirements permit. Such flight designs should consider a priori knowledge of focal species’ behavioral characteristics. Research is needed to determine whether any such disturbance is a result of visual or auditory stimuli.     

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.     

A comparison of image and observer based aerial surveys of narwhal

From 25 to 30 August 2014 a double‐observer line‐transect survey was conducted over Melville Bay, home to one of two summering populations of narwhal (Monodon monoceros) off West Greenland. A total of 1,932 linear kilometers was surveyed along 33 transects. In addition to using observers, the aircraft was equipped with two oblique cameras to capture a comparable data set. Analysts reviewed the images for narwhal sightings, which were then matched to the observer sightings. The objectives of the study were to determine advantages and disadvantages of the detection capabilities of both methodologies, and to conduct a comparative analysis of population abundance estimates. Correcting for the truncated detection distance of the images (500 m), the image analysts recorded more sightings (62) and a lower mean group size (2.2) compared to aerial observers (36 and 3.5, respectively), resulting in comparable numbers of individuals detected by both platforms (135 vs. 126). The abundance estimate based on the image sightings was 2,536 (CV = 0.51, 95% CI: 1,003–6,406), which was not significantly different from the aerial observers estimate of 2,596 individuals (CV = 0.51; 95% CI: 961–7,008). This study supports the potential of using UAS for marine mammal abundance studies.     

云南西双版纳尚勇保护区亚洲象对栖息地的选择

以西双版纳国家级自然保护区尚勇子保护区内的亚洲象种群为研究对象, 利用村寨调查、样线调查和3S(GIS、GPS、RS) 技术对该地区野生亚洲象的栖息地状况和亚洲象对栖息地选择利用进行了初步的研究。结果表明亚洲象喜欢海拔1 000 m以下的区域, 坡度小于10°的区域, 坡位为平坦的沟谷和山坡的下部, 坡向为南、北两个方向。偏好的植被类型有竹阔混交林、灌丛和高山草甸。研究还发现尚勇保护区及周边区域亚洲象栖息地的丧失和日益增加的非法盗猎活动已经严重威胁到该地区野生亚洲象种群的生存。     

Evaluating performance of aerial survey data in elephant habitat modelling

Aerial survey data are widely used to model distribution of wildlife. However, their performance in habitat modelling remains largely untested. We used aerial survey and satellite‐linked Global Positioning System (GPS) collar data for elephants, to test (i) whether there is an optimal spatial resolution of predictor variables at which habitat models based on aerial survey data that are uncorrected for locational error can accurately predict elephant habitat and (ii) whether habitat models based on these data sets can accurately predict the presence of elephants in closed woodland habitats. We applied maximum entropy modelling (Maxent) to these data sets and used the Normalised Difference Vegetation Index (NDVI) as well as distance from water points as the habitat predictors to answer these questions. Our results demonstrate better ability of aerial survey data to predict elephant presence at the coarser spatial resolution of 1000 m of both predictor variables. Habitat models derived from aerial survey data underpredicted elephant presence in more closed woodland habitats than those derived from GPS collar data. This result implies that elephants located under dense tree canopies are likely missed during an aerial survey. Our study is one of the first to empirically test and report results on the poor performance of aerial survey data in habitat modelling especially in dense woodlands.     

A population survey of forest elephants (Loxodonta africana cyclotis) in northern Congo

From February to April of 1989 transects were run in four forested areas of northern Congo (Brazzaville) to census elephant populations using dung counts. A total of 401.0 km of line transects was completed during the survey. An overall extrapolated density of 0.7 elephants/km² was obtained with a variation from 0.3 to 0.9 elephants/km². This study shows for the first time that elephants occur in high densities in a number of sites in northern Congo. When elephant dung density is plotted against distance from the nearest village for each transect a significant positive linear correlation results. Based on this correlation we hypothesize that the population of elephants in much of northern Congo is high. Local reports and our own observations indicate that elephant poaching is heavy in northern Congo and that the CITES ban on trade in African elephant ivory has had a limited effect on the level of poaching.     

People and wildlife: Conflicts arising around the Bia Conservation Area in Ghana

Human–wildlife conflicts have drastically increased around conservation areas in Africa over the last decades. Underlying causes are similar across regions and can most often be attributed to land-use changes and increasing human populations. However, wildlife species composition involved in conflicts can vary tremendously across sites. Conflict assessment often focuses on prominent species such as elephants or other large mammals, and information on smaller wildlife species involved in conflicts is often lacking. We conducted 100 interviews in 10 communities around the Bia Conservation Area (BCA), Ghana, to address the type and extent of damage as well as possible factors influencing the risk of crop-raids. We assessed wildlife presence and relative densities through indirect observations on transects and spoor-plots in the transition zone between BCA and agricultural land. Crop-raiding was present across all communities interviewed but was more than twice as high in the north compared to the south. Squirrels (Sciuridae spp.) were the most frequent crop-raiding species (92% of farmers experienced damage), but elephants (Loxodonta africana cyclotis [Matschie, 1900]) were nevertheless the major conflict generating species (84% of farmers experienced damage). Squirrel signs were 75 times as frequent as any other species recorded in the farmland. Cocoa (Theobroma cacao) was most frequently raided (99%), followed by cassava (Manihot esculenta) and yam (Dioscorea spp.). Raiding might have been promoted through inappropriate agricultural practices while only few current mitigation strategies have been successful so far. Promising strategies include chili fences and buffer zones, in which less susceptible crops such as chili pepper are planted. Further education of farmers on mitigation measures is needed, including training on improved farming practices.     

Elephants caught in the middle: impacts of war, fences and people on elephant distribution and abundance in the Caprivi Strip, Namibia

We conducted wet [26 March–4 April 2003 (Apr03)] and dry [1–8 November 2005 (Nov05)] season aerial surveys of African elephants ( Loxodonta africana Blumenbach) in the Caprivi Strip, Namibia to provide an updated status report on elephant numbers and distribution and assist with a historical analysis of elephant distribution and abundance in the Caprivi Strip. During the wet season when water was available in seasonal pans, elephants were widely distributed throughout the survey area. In contrast, during the dry season, a majority of elephant herds occurred within 30 km of the perennial Kwando, Linyanti and Okavango rivers and few herds occurred within the West Caprivi Game Reserve where water in the seasonal pans was limited. We estimated 5318 elephants for the 7731-km² survey area (0.71 elephants km⁻²) for the Apr03 wet season survey and 6474 elephants for the 8597-km² survey area (0.75 elephants km⁻²) for the Nov05 dry season survey. Based on our aerial surveys and reports of elephant numbers and distribution from historical aerial surveys and telemetry studies, civil war, veterinary fences and human activities appear to have effected changes in African elephant abundance, distribution and movements in the Caprivi Strip, Namibia since 1988 when the first comprehensive aerial surveys were conducted.     

Using sample aerial surveys to estimate the abundance of the endangered Grevy’s zebra in northern Kenya

The effective management of endangered mammals requires reliable estimates of population size. This is challenging for species such as Grevy’s zebra (Equus grevyi) that are distributed over large areas at low densities. Less than 2500 Grevy’s zebra remain in the wild, scattered across 85,000 km² of savannah in northern Kenya and Ethiopia. An efficient, accurate and repeatable survey method is required to guide conservation planning for the species. Currently, total aerial counts are used to census endangered species within Kenya, but are costly in terms of resources. In this study, we evaluated the suitability of sample survey methods for Grevy’s zebra. We estimated population size using sample aerial counts for a known population of Grevy’s zebra in Lewa Wildlife Conservancy (LWC), providing the opportunity to test the accuracy of sample methods, while comparing resource costs with total count methods. We sampled one‐third of LWC using parallel 500‐ m strip transects at 1500‐ m intervals. The population estimate was comparable to the known population size and was less than half as expensive as the equivalent total count survey. Our results suggest sample aerial surveys provide an accurate and cost‐effective means of monitoring Grevy’s zebra and other endangered species in open habitats.     

黑龙江省三江平原丹顶鹤的数量分布

在地面调查的基础上,我们使用Y-11轻型飞机对黑龙江省三江平原地区的丹顶鹤的数量分布近行了调查,调查时飞行高度80米,航速140公里/小时,续航里程共3748公里。调查结果表明,丹顶鹤在三江平原主要分布在8个地区,其中嘟噜河下游、洪河自然保护区、七星河流域和兴凯湖低地是主要繁殖地,总数量共309只。     

How does a re-colonizing population of Asian elephants affect the forest habitat?

The Asian elephant Elephas maximus is currently re-colonizing the Bardia National Park in lowland Nepal. We studied their impact on woody vegetation in the nutrient-rich floodplain and in the relatively nutrient-poor sal forest. The types and extent of tree impact were recorded along fixed-width transects (335 km). Species composition, density and size classes ≥8 cm diameter breast height (dbh) were recorded in 15-m radius random plots ( n =95). Impact was higher in the floodplain complex than in the sal-dominated forest. Our hypothesis that elephants were more selective on species in the nutrient-poor sal forest was only partly supported; the niche breadth of impacted trees was slightly higher in the floodplain complex. Pushed-over trees accounted for the highest proportion of impact (55%), followed by killed trees (39%). Of the pushed trees, 10% were not used for food. Among food trees, elephants selectively impacted size class 12–16 cm dbh, whereas non-food trees were impacted independently of size. A large proportion of the freshly browsed trees had been felled previously, indicating that most felled trees survived, enabling elephants to feed on them again. This may reflect an evolutionary adaptation among long-lived species with high site fidelity. Owing to preferential use but low abundance, two species in sal forest, Grewia spp. and Desmodium oojeinense , were found to be particularly vulnerable to local extinction due to elephants. Although the elephants had impacted a large number of species (62, 73% of all), 56.4% of the impacted trees consisted of Mallotus phillippinensis . A recently observed increase in the density of M. phillippinensis and the concurrent reduction of the hardly utilized Shorea robusta indicates that the rapidly growing elephant population may modify the composition of the forest by increasing its preferred food species.     

埃塞俄比亚Alatish国家公园大型哺乳动物多样性、分布及其与栖息地的联系

于2004年8月-2005年3月间研究了Alatish国家公园在雨季和旱季期间大型哺乳动物的多样性和分布.基于地形图、卫片和预调查结果,在4种生境类型中识别出37个100 km2或者更小的斑块.在4个植被类型中随机选择了11个斑块,沿随机选择的样线进行了调查,样线10 km 长,0.2 km宽,覆盖了该区域面积的20%.使用Shannon-Wiener多样性指数、DISTANCE 5.0 软件以及 Simpson相似性指数分析了大型哺乳动物的多样性以及分布的相似性.记录了20种体型较大的哺乳动物;河边林地在雨季和旱季都有最高的多样性,林地的多样性最低;旱季中4种类型的栖息地具有相似的分布型;由于栖息地退化不能确定物种的分布格局.联合距离取样分析揭示出,旱季和雨季大型哺乳动物的密度较低,分别为4.067只/km2和6.263只/km2.所观察的哺乳动物可分为草食动物(15%)、游牧动物(30%)、杂食动物(25%)与食肉动物(30%).     

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.     

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.     

Computer‐automated bird detection and counts in high‐resolution aerial images: a review

Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.     

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)     

An empirical evaluation of the African elephant as a focal species for connectivity planning in East Africa

Aim Large, charismatic and wide‐ranging animals are often employed as focal species for prioritizing landscape linkages in threatened ecosystems (i.e. ‘connectivity conservation’), but there have been few efforts to assess empirically whether focal species co‐occur with other species of conservation interest within potential linkages. We evaluated whether the African elephant (Loxodonta africana), a world‐recognized flagship species, would serve as an appropriate focal species for other large mammals in a potential linkage between two major protected area complexes. Location A 15,400 km² area between the Ruaha and Selous ecosystems in central Tanzania, East Africa. Methods We used walking transects to assess habitat, human activity and co‐occurrence of elephants and 48 other large mammal species (> 1 kg) at 63 sites using animal sign and direct sightings. We repeated a subset of transects to estimate species detectability using occupancy modelling. We used logistic regression and AIC model selection to characterize patterns of elephant occurrence and assessed correlation of elephant presence with richness of large mammals and subgroups. We considered other possible focal species, compared habitat‐based linear regression models of large mammal richness and used circuit theory to examine potential connectivity spatially. Results Elephants were detected in many locations across the potential linkage. Elephant presence was highly positively correlated with the richness of large mammals, as well as ungulates, carnivores, large carnivores and species > 45 kg in body mass (‘megafauna’). Outside of protected areas, both mammal richness and elephant presence were negatively correlated with human population density and distance from water. Only one other potential focal species was more strongly correlated with species richness than elephants, but detectability was highest for elephants. Main conclusions Although African elephants have dispersal abilities that exceed most other terrestrial mammals, conserving elephant movement corridors may effectively preserve habitat and potential landscape linkages for other large mammal species among Tanzanian reserves.