Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments

Airborne LiDAR (Light Detection and Ranging) is a remote sensing technology that offers the ability to collect high horizontal sampling densities of high vertical resolution vegetation height data, over larger spatial extents than could be obtained by field survey. The influence of vegetation structure on the bird is a key mechanism underlying bird–habitat models. However, manual survey of vegetation structure becomes prohibitive in terms of time and cost if sampling needs to be of sufficient density to incorporate fine-grained heterogeneity at a landscape extent. We show that LiDAR data can help bridge the gap between grain and extent in organism–habitat models. Two examples are provided of bird–habitat models that use structural habitat information derived from airborne LiDAR data. First, it is shown that data on crop and field boundary height can be derived from LiDAR data, and so have the potential to predict the distribution of breeding Sky Larks in a farmed landscape. Secondly, LiDAR-retrieved canopy height and structural data are used to predict the breeding success of Great Tits and Blue Tits in broad-leaved woodland. LiDAR thus offers great potential for parameterizing predictive bird–habitat association models. This could be enhanced by the combination of LiDAR data with multispectral remote sensing data, which enables a wider range of habitat information to be derived, including both structural and compositional characteristics.     

Mapping migratory bird prevalence using remote sensing data fusion

Background

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA.

Methodology and Principal Findings

A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species.

Conclusion and Significance

Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.     

基于站点的生物多样性星空地一体化遥感监测

科学制定生物多样性保护和恢复政策, 需要空间上连续、时间上高频的物种和生境分布以及物种迁移信息支持, 遥感是目前能满足该要求的有效技术手段。近年来, 遥感平台和载荷技术高速发展, 综合多平台、多尺度、多模式遥感技术, 开展基于站点的星空地一体化遥感观测试验, 可以对地表进行时空多维度、立体连续观测, 为生物多样性遥感监测提供了新的契机。本文总结了使用遥感技术监测生物多样性的主要方法, 回顾了典型的星空地一体化遥感观测试验。综述以往研究发现, 一方面, 现有遥感试验还缺少对生物多样性直接监测指标的观测, 另一方面, 生物多样性遥感监测方法也缺少星空地多维立体观测平台的支撑, 亟需加强两者的融合, 开展基于站点的生物多样性星空地一体化遥感监测研究。以设于我国四川王朗大熊猫国家级自然保护区内的王朗山地生态遥感综合观测试验站为例, 展示了星空地一体化遥感综合观测试验平台在生物多样性监测中的应用潜力。星空地一体化遥感观测可以提供物种和生境的综合定量信息, 与生态模型有机结合, 可以刻画生物多样性的时空格局与动态过程, 有助于挖掘过程机理, 提高生物多样性监测的信息化水平。     

Conspecific Attraction is a Missing Component in Wildlife Habitat Modeling

Abstract Wildlife biologists use knowledge about wildlife-habitat relationships to create habitat models to predict species occurrence across a landscape. Researchers attribute limitations in predictive ability of a habitat model to data deficiencies, missing parameters, error introduced by specifications of the statistical model, and natural variation. Few wildlife biologists, however, have incorporated intra- and interspecific interactions (e.g., conspecific attraction, competition, predator-prey relationships) to increase predictive accuracy of habitat models. Based on our literature review and preliminary data analysis, conspecific attraction can be a primary factor influencing habitat selection in wildlife. Conspecific attraction can lead to clustered distributions of wildlife within available habitat, reducing the predictive ability of habitat models based on vegetative and geographic parameters alone. We suggest wildlife biologists consider incorporating a parameter in habitat models for the clustered distribution of individuals within available habitat and investigate the mechanisms leading to clustered distributions of species, especially conspecific attraction.     

Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species

Remote sensing (RS) data may play an important role in the development of cost-effective means for modelling, mapping, planning and conserving biodiversity. Specifically, at the landscape scale, spatial models for the occurrences of species of conservation concern may be improved by the inclusion of RS-based predictors, to help managers to better meet different conservation challenges. In this study, we examine whether predicted distributions of 28 red-listed plant species in north-eastern Finland at the resolution of 25 ha are improved when advanced RS-variables are included as unclassified continuous predictor variables, in addition to more commonly used climate and topography variables. Using generalized additive models (GAMs), we studied whether the spatial predictions of the distribution of red-listed plant species in boreal landscapes are improved by incorporating advanced RS (normalized difference vegetation index, normalized difference soil index and Tasseled Cap transformations) information into species-environment models. Models were fitted using three different sets of explanatory variables: (1) climate-topography only; (2) remote sensing only; and (3) combined climate-topography and remote sensing variables, and evaluated by four-fold cross-validation with the area under the curve (AUC) statistics. The inclusion of RS variables improved both the explanatory power (on average 8.1 % improvement) and cross-validation performance (2.5 %) of the models. Hybrid models produced ecologically more reliable distribution maps than models using only climate-topography variables, especially for mire and shore species. In conclusion, Landsat ETM+ data integrated with climate and topographical information has the potential to improve biodiversity and rarity assessments in northern landscapes, especially in predictive studies covering extensive and remote areas.     

Temporal transferability of wildlife habitat models: implications for habitat monitoring

Aim Temporal transferability is an important issue when habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of habitat monitoring. While the combination of remote sensing technology and habitat modelling provides a useful tool for habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite‐derived variables affect temporal transferability of habitat models and their usefulness for habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single‐year or multi‐year (i.e. 3‐year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda habitat within and across time periods by using threshold‐independent and threshold‐dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda habitat. However, the models developed using multi‐year data exhibited significantly higher temporal transferability than those developed using single‐year data. In addition, models developed with phenology metrics, especially when using multi‐year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with habitat modelling constitutes a suitable tool for characterizing wildlife habitat and monitoring its temporal dynamics. Using multi‐year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter‐annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for habitat monitoring through the integration of remote sensing technology and habitat modelling, which may be useful for the conservation of the giant panda and many other species.     

全球气候变化对野生动物的影响

人类活动所引起的温室气体增加以及由此造成的全球气候变化和对全球生态环境的影响正越来越引起人们的关注,在全球气候变化对野生动物影响的研究中发现,随着全球气温变暖,野生动物的分布区整体上向北移,物修期提前,动物的繁殖及其种群大小,不同的种类做出不同的响应,有的受益于全球变暖,繁殖增加,成活率高,种群壮大,有的受制于这一变化,种群逐渐缩小甚至面临灭绝的威胁,总的来看,全球气候变暖使更多的野生动物无所适从,因此,加强对气候变化在不同层面上对野生动物影响机制的研究,调整野生动物保护措施,对野生动物及其生境的保护,维持生态系统多样性将显得十分重要。     

Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics

Infectious disease ecology has recently raised its public profile beyond the scientific community due to the major threats that wildlife infections pose to biological conservation, animal welfare, human health and food security. As we start unravelling the full extent of emerging infectious diseases, there is an urgent need to facilitate multidisciplinary research in this area. Even though research in ecology has always had a strong theoretical component, cultural and technical hurdles often hamper direct collaboration between theoreticians and empiricists. Building upon our collective experience of multidisciplinary research and teaching in this area, we propose practical guidelines to help with effective integration among mathematical modelling, fieldwork and laboratory work. Modelling tools can be used at all steps of a field-based research programme, from the formulation of working hypotheses to field study design and data analysis. We illustrate our model-guided fieldwork framework with two case studies we have been conducting on wildlife infectious diseases: plague transmission in prairie dogs and lyssavirus dynamics in American and African bats. These demonstrate that mechanistic models, if properly integrated in research programmes, can provide a framework for holistic approaches to complex biological systems.     

Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data

• 1 Environmental heterogeneity is important in determining the distribution and abundance of organisms at various spatial scales. The ability to understand and predict distribution patterns is important for solving many management problems in conservation biology and wildlife epidemiology.
• 2 The badger Meles meles is a highly adaptable, medium‐sized carnivore, distributed throughout temperate Eurasia, which shows a wide diversity of social and spatial organization. Within Britain, badgers are not only legally protected, but they also serve as a wildlife host for bovine tuberculosis Mycobacterium bovis. An evaluation of the role of badgers in the dynamics of this infection depends on understanding the responses of badgers to the environment at different spatial scales.
• 3 The use of digital data to provide information on habitats for distribution models is becoming common. Digital data are increasingly accessible and are generally cheaper than field surveys. There has been little research, however, to compare the accuracy of models based on field‐derived and remotely derived data.
• 4 In this paper, we make quantified comparisons between large‐scale presence/absence models for badgers in Britain, based on field‐surveyed habitat data and remotely derived digital data, comprising elevation, geology and soil.
• 5 We developed four models: 1980s badger survey data using field‐based and digital data, and 1990s badger survey data using field‐based and digital data. We divided each of the four datasets into two subsets and used one subset for training (developing) the model and the other for testing it.
• 6 All four training models had classification accuracies in excess of 69%. The models generated from digital data were slightly more accurate than those generated from field‐derived habitat data.
• 7 The high classificatory ability of the digital‐based models suggests that the use of digital data may overcome many of the problems associated with field data in wildlife‐habitat modelling, such as cost and restricted geographical coverage, without any significant impact on model performance for some species. The more widespread use of digital data in wildlife‐habitat models should enhance their accuracy, repeatability and applicability and make them better‐suited as tools to aid policy‐ and decision‐making processes.

     

Building better wildlife-habitat models

Wildlife-habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data., and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife-habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta-model, and discuss methods for their validation. Even where all six assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta-model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead.     

State-space model combining local camera data and regional administration data reveals population dynamics of wild boar

Recent increases in wildlife cause negative impacts on humans through both economic and ecological damage, as well as the spread of pathogens. Understanding the population dynamics of wildlife is crucial to develop effective management strategies. However, it is difficult to estimate accurate and precise population size over large spatial and temporal scales because of the limited data availability. We addressed these issues by first fitting a random encounter and staying time (REST) model based on camera trap data to construct an informative prior distribution for a capture rate parameter in a harvest-based Bayesian state-space model. We constructed a Bayesian state-space model that integrated administration data on the number of captured wild boar with the prior distribution of capture efficiency estimated by camera trap data. The model with informative prior distribution from the REST model successfully estimated population dynamics, whereas the model using only the administration data did not, owing to a lack of parameter convergence. We identified areas where (1) wild boars exhibit a high potential population growth rate and a high carrying capacity, (2) current trapping efforts are effectively suppressing local populations, and (3) trapping reinforcement is required to control populations in the whole region. The model could be used to predict future trends in populations under the assumptions of ongoing trapping pressure. This will help identify spatially explicit trapping efforts to achieve target population levels.     

基于熵值法的我国野生动物资源可持续发展研究

在构建我国野生动物资源可持续发展指标体系、采用熵值法确定各指标权重的基础上,研究了野生动物及栖息地的保护与资源经济协调发展情况,分析了野生动物资源可持续发展的总体运行趋势.结果表明:(1) 2001-2009年,我国野生动物及栖息地保护综合指数、资源经济综合指数和社会进步综合指数总体呈上升趋势.(2)2001-2005年、2007-2009年,野生动物及栖息地保护与资源经济的协调发展度值在0.14-0.35之间,整体协调发展水平较低,属于失调衰退类;2006年的协调发展度值为0.45,处于保护与利用协调发展的过渡阶段.(3)野生动物资源可持续发展水平呈上升趋势,但总体水平不高,2001-2005年的可持续发展综合指数在0.15-0.31之间,属于传统较落后的发展阶段,2007、2008年的可持续发展综合指数分别为0.45和0.47,属于传统较落后的发展阶段;2006年、2009年的可持续发展综合指数分别为0.55和0.56,实现了由传统发展向可持续发展的过渡.     

Empirically simulated study to compare and validate sampling methods used in aerial surveys of wildlife populations

This paper compares the distribution, sampling and estimation of abundance for two animal species in an African ecosystem by means of an intensive simulation of the sampling process under a geographical information system (GIS) environment. It focuses on systematic and random sampling designs, commonly used in wildlife surveys, comparing their performance to an adaptive design at three increasing sampling intensities, using the root mean square errors (RMSE). It further assesses the impact of sampling designs and intensities on estimates of population parameters. The simulation is based on data collected during a prior survey, in which geographical locations of all observed animals were recorded. This provides more detailed data than that usually available from transect surveys. The results show precision of estimates to increase with increasing sampling intensity, while no significant differences are observed between estimates obtained under random and systematic designs. An increase in precision is observed for the adaptive design, thereby validating the use of this design for sampling clustered populations. The study illustrates the benefits of combining statistical methods with GIS techniques to increase insight into wildlife population dynamics.     

The use of on‐animal acoustical recording devices for studying animal behavior

Audio recordings made from free‐ranging animals can be used to investigate aspects of physiology, behavior, and ecology through acoustic signal processing. On‐animal acoustical monitoring applications allow continuous remote data collection, and can serve to address questions across temporal and spatial scales. We report on the design of an inexpensive collar‐mounted recording device and present data on the activity budget of wild mule deer (Odocoileus hemionus) derived from these devices applied for a 2‐week period. Over 3300 h of acoustical recordings were collected from 10 deer on their winter range in a natural gas extraction field in northwestern Colorado. Analysis of a subset of the data indicated deer spent approximately 33.5% of their time browsing, 20.8% of their time processing food through mastication, and nearly 38.3% of their time digesting through rumination, with marked differences in diel patterning of these activities. Systematic auditory vigilance was a salient activity when masticating, and these data offer options for quantifying wildlife responses to varying listening conditions and predation risk. These results (validated using direct observation) demonstrate that acoustical monitoring is a viable and accurate method for characterizing individual time budgets and behaviors of ungulates, and may provide new insight into the ways external forces affect wildlife behavior.     

Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop‐raiding

Few universal trends in spatial patterns of wildlife crop‐raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human–wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop‐raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop‐raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P‐values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop‐raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop‐raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio‐ecological drivers of wildlife crop‐raiding is paramount for designing mitigation and land‐use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.     

Cultivation and Conservation in Ngorongoro Conservation Area, Tanzania

Ngorongoro Conservation Area (NCA), Tanzania, contains renowned wildlife, an expanding human population, and cultivation by Maasai agro-pastoralists and non-Maasai agriculturalists. We used integrated assessments to explore some effects of cultivation on livestock, resident wildlife, and people. Using a Landsat image from 2000, we mapped 3,967 ha [9,803 acres (ac)] of cultivation within NCA, or 39.7 km² of the 8,283 km² conservation area. Using integrative ecosystem (Savanna) and household (PHEWS) models, we assessed effects of: up to 50,000 ac (20,234 ha) of cultivation; cultivation concentrated into two blocks totaling 10,000 ac (4,047 ha) and 20,000 ac (8,094 ha) that may be more palatable to tourists; and human population growth. Simulations with from 10,000 to 50,000 ac in cultivation showed no large changes in ungulate populations relative to there being no cultivation. When cultivation was altered to be in two blocks, some wildlife populations changed (≤15%) and ungulate biomass remained the same. When cultivation was increased linearly with human population, poor households needed 25% of their diets to come from relief as populations tripled, because livestock could not increase significantly. Our results indicate that having <1% of NCA in cultivation, in its current distribution, is not overly detrimental to wildlife or livestock populations, and is important to Maasai well-being.     

Comparing motion capture cameras versus human observer monitoring of mammal movement through fence gaps: a case study from Kenya

Monitoring the movement and distribution of wildlife is a critical tool of an adaptive management framework for wildlife conservation. We installed motion‐triggered cameras to capture the movement of mammals through two purpose‐built migration gaps in an otherwise fenced conservancy in northern Kenya. We compared the results to data gathered over the same time period (1 Jan 2011–31 Dec 2012) by the human observers monitoring mammal tracks left at the same fence gaps in a sandy loam detection strip. The camera traps detected more crossing events, more species and more individuals of each species per crossing event than did the human track observers. We tested for volume detection differences between methods for the five most common species crossing each gap and found that all detection rates were heavily weighted towards the camera‐trap method. We review some of the discrepancies between the methods and conclude that although the camera traps record more data, the management of that data can be time‐consuming and ill‐suited to some time‐sensitive decision‐making. We also discuss the importance of daily track monitoring for adaptive management conservation and community security.     

地形对植被生物量遥感反演的影响——以广州市为例

目前植被生物量遥感反演研究中的地形校正主要是校正地形变化对地表反射率的影响,较少考虑地形起伏引起的像元面积与实际地表面积的差异,而这种差异将导致植被生物量估算结果的偏差.在生物量遥感反演的基础上,结合地表面积计算模型和物质守恒定律,建立了生物量地形校正模型,定量分析和讨论了地形起伏对广州市植被类型面积提取和生物量准确估算的影响.结果表明:地形校正前后全市针叶林、阔叶林、草地、灌木林和园地面积分别增加6.18％、3.70％、2.86％、1.92％和1.29％;在综合分析区域生物量遥感反演中的各种不确定性的基础上,建立的各植被类型的生物量模型均具有较高精度,相关系数均接近或者超过0.9,可以满足生物量反演的要求;全市植被生物量呈现出东、北高,西、南低的分布格局,像元实际代表的林地(阔叶林和针叶林)平均生物量为61.86t/hm2,高于珠三角区域生物量平均值,但与亚热带林的顶级群落生物量水平有较大差距,林地生物量还有较大的增长空间;经过校正地形变化引起的像元面积和实际地表面积差异对生物量提取结果的影响后,植被总生物量比校正前增加了5.82％,5种植被类型的总生物量有不同程度的增加,阔叶林、针叶林、草地、灌木林和园地分别增加了7.74％,4.76％、3.34％、2.50％和1.58％.与其它的表面积计算模型相比,利用的像元地表面积模型具有较高的精度,可以满足生物量遥感估算中地形校正的需要.     

基于遥感与模型耦合的冬小麦生长预测

遥感的空间性、实时性与作物生长模型的过程性、机理性优势互补,将两者有效耦合已成为提高作物生长监测预测能力的重要手段之一。提出了一种基于地空遥感信息与生长模型耦合的冬小麦预测方法,该方法基于初始化/参数化策略,以不同生育时期的小麦叶面积指数(LAI)和叶片氮积累量(LNA)为信息融合点将地面光谱数据(ASD)及HJ-1 A/B CCD、Landsat-5 TM数据与冬小麦生长模型(WheatGrow)耦合,反演得到区域尺度生长模型运行时难以准确获取的部分管理措施参数(播种期、播种量和施氮量),在此基础上实现了对冬小麦生长的有效预测。实例分析结果表明,LNA较LAI对模型更敏感,以之作为耦合点的反演效果较好。另外,抽穗期是遥感信息与生长模型耦合的最佳时机,对播种期、播种量和施氮量反演的RMSE值分别达到5.32 d、14.81 kg/hm²、14.11 kg/hm²。生长模型与遥感耦合后的模拟结果很好地描述了冬小麦长势和生产力指标的时空分布状况,长势指标的模拟相对误差小于0.25,籽粒产量模拟的相对误差小于0.1。因此研究结果可为区域尺度冬小麦生长的监测预测提供重要理论依据。     

Evaluating model transferability for a threatened species to adjacent areas: Implications for rock‐wallaby conservation

When modelling the distribution of a species, it is often not possible to comprehensively sample the whole distribution of the species and managers may have habitat models based on data from one area that they want to apply in other areas. Hence, an important question is: how accurate are models of the distributions of species when applied beyond the areas where they were developed? A first step in measuring model transferability could be testing models in adjacent areas. We predicted the habitat associations of the brush‐tailed rock‐wallaby (Petrogale penicillata) across two spatial scales in two neighbouring study areas in eastern Australia, south‐east Queensland and north‐east New South Wales. We used classification trees for exploratory data analysis of habitat relationships and then applied logistic regression models to predict species occurrence. We assessed the within‐area discriminative ability of the habitat models using cross‐validation and threshold plots, and tested the predictive ability of the models for adjacent areas using the receiver operating characteristic statistic to determine the area under the curve. We found that models performed well within an area and extrapolating them to adjacent areas resulted in good predictive performance at the site scale but substantially poorer predictive performance at the landscape scale. We conclude that distribution models for wildlife species should only be extrapolated to neighbouring areas with caution when using landscape‐scale environmental variables. Alternatively, only key habitat associations predicted by the models at this scale should be transferred across adjacent areas once verified against local knowledge of the ecology of the study species.