Characterization of an alpine tree line using airborne LiDAR data and physiological modeling

Understanding what environmental drivers control the position of the alpine tree line is important for refining our understanding of plant stress and tree development, as well as for climate change studies. However, monitoring the location of the tree line position and potential movement is difficult due to cost and technical challenges, as well as a lack of a clear boundary. Advanced remote sensing technologies such as Light Detection and Ranging (LiDAR) offer significant potential to map short individual tree crowns within the transition zone despite the lack of predictive capacity. Process‐based forest growth models offer a complementary approach by quantifying the environmental stresses trees experience at the tree line, allowing transition zones to be defined and ultimately mapped. In this study, we investigate the role remote sensing and physiological, ecosystem‐based modeling can play in the delineation of the alpine tree line. To do so, we utilize airborne LiDAR data to map tree height and stand density across a series of altitudinal gradients from below to above the tree line within the Swiss National Park (SNP), Switzerland. We then utilize a simple process‐based model to assess the importance of seasonal variations on four climatically related variables that impose non‐linear constraints on photosynthesis. Our results indicate that all methods predict the tree line to within a 50 m altitudinal zone and indicate that aspect is not a driver of significant variations in tree line position in the region. Tree cover, rather than tree height is the main discriminator of the tree line at higher elevations. Temperatures in fall and spring are responsible for the major differences along altitudinal zones, however, changes in evaporative demand also control plant growth at lower altitudes. Our results indicate that the two methods provide complementary information on tree line location and, when combined, provide additional insights into potentially endangered forest/grassland transition zones.     

赤腹松鼠（Callosciurus erythralus）春季生境特征初步分析

2009年2月至5月,在广西龙江河畔对赤腹松鼠（C.erythralus）的春季生境特征进行了分析.野外共测量了57个10m×10m样方中的13个生态因子,并运用频次分析和主成分分析的方法,对赤腹松鼠的春季生境选择因子进行了分析.结果表明,赤腹松鼠春季生境的主要特征为：郁闭度良好,水源距离＜30m,坡度20～40°,避风性良好,坡向以东坡和南坡为主,坡位中坡位或上坡位,食物因子良好,人为干扰距离＜10m,海拔50～100m,乔木密度＜50株,乔木距离低于4m,灌木密度低于200株,灌木距离＜2m.影响赤腹松鼠春季生境选择的主要因子为郁闭度、避风性、坡度、坡位和灌木距离;次要因子为海拔、人为干扰距离、乔木距离、水源距离、乔木密度、灌木密度、食物丰富度、坡向.     

遥感与GIS支持下的崇明东滩迁徙鸟类生境适宜性分析

崇明东滩鸟类自然保护区位于长江入海口,是国际迁徙鸟类重要的栖息地.受自然和社会因素影响,该地区鸟类栖息环境正处于快速变化.研究采用面向对象的图像分割方法提取鸟类生境适宜性多边形评价单元,根据近年来野外调查数据分析了影响迁徙鸟类生存的地类、植被、潮沟、底栖生物等关键环境因素,建立了鸟类与关键环境影响因素的定性定量关系.在此基础上利用GIS空间分析方法和技术,进行了崇明东滩鸟类保护区内主要四大鸟类种群雁鸭类、鸻鹬类、鹭类以及鸥类的生境适宜性分析.结果表明:(1)崇明东滩迁徙鸟类生境较适宜的面积占保护区总面积40%左右;(2)光滩区域、与光滩邻近的海三棱藨草带以及潮沟地带是鸟类生境适宜性较好的地理区域;(3)基于面向对象的遥感分析技术和GIS空间分析技术,能有效且简便地对生态环境处于快速动态变化中的物种生境适宜性进行快速、客观准确的分析评价,其结果可为崇明东滩鸟类种群及其生存环境规划、保护和管理提供基础科学依据.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

Toward community predictions: Multi‐scale modelling of mountain breeding birds' habitat suitability,landscape preferences,and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence‐only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi‐scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including “Bio11” (Mean Temperature of Coldest Quarter), and “Bio 4” (Temperature Seasonality), then in the focal variables including “Forest”, “Orchard”, and “Agriculture area” as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.     

A proposed strategy for maintaining mature forest habitat in Tasmania's wood production forests

Mature forests have structural habitat features that can take hundreds of years to develop, and large reserves alone are unlikely to ensure conservation of the species that rely on these features. This paper outlines a proposed new approach to managing mature forest features, the ‘mature habitat management approach’, in areas outside of reserves. The objective was to maintain a network of current and future mature forest habitat distributed across the landscape. The approach is designed to complement the existing reserve network and management actions and is tenure‐blind. Spatial information on the availability of mature forest habitat at the local (1‐km radius) and landscape (5‐km radius) scales is used for decisions on retention within a 1‐km radius of a harvest area, to reach the minimum target of 20% and 30% retention of mature forest at the local and landscape spatial scales, respectively. We believe this approach could contribute to meeting the conservation needs of many species that require mature forest features for refuge and breeding in Tasmania and elsewhere.     

Remote sensing‐based landscape indicators for the evaluation of threatened‐bird habitats in a tropical forest

Avian species persistence in a forest patch is strongly related to the degree of isolation and size of a forest patch and the vegetation structure within a patch and its matrix are important predictors of bird habitat suitability. A combination of space‐borne optical (Landsat), ALOS‐PALSAR (radar), and airborne Light Detection and Ranging (LiDAR) data was used for assessing variation in forest structure across forest patches that had undergone different levels of forest degradation in a logged forest—agricultural landscape in Southern Laos. The efficacy of different remote sensing (RS) data sources in distinguishing forest patches that had different seizes, configurations, and vegetation structure was examined. These data were found to be sensitive to the varying levels of degradation of the different patch categories. Additionally, the role of local scale forest structure variables (characterized using the different RS data and patch area) and landscape variables (characterized by distance from different forest patches) in influencing habitat preferences of International Union for Conservation of Nature (IUCN) Red listed birds found in the study area was examined. A machine learning algorithm, MaxEnt, was used in conjunction with these data and field collected geographical locations of the avian species to identify the factors influencing habitat preference of the different bird species and their suitable habitats. Results show that distance from different forest patches played a more important role in influencing habitat suitability for the different avian species than local scale factors related to vegetation structure and health. In addition to distance from forest patches, LiDAR‐derived forest structure and Landsat‐derived spectral variables were important determinants of avian habitat preference. The models derived using MaxEnt were used to create an overall habitat suitability map (HSM) which mapped the most suitable habitat patches for sustaining all the avian species. This work also provides insight that retention of forest patches, including degraded and isolated forest patches in addition to large contiguous forest patches, can facilitate bird species retention within tropical agricultural landscapes. It also demonstrates the effective use of RS data in distinguishing between forests that have undergone varying levels of degradation and identifying the habitat preferences of different bird species. Practical conservation management planning endeavors can use such data for both landscape scale monitoring and habitat mapping.     

基于多光谱影像的森林树种识别及其空间尺度响应

当前,不同空间分辨率卫星影像对森林类型识别结果中普遍存在的尺度效应,而且纹理参量对不同尺度下树种识别精度的影响仍缺乏广泛认知.本研究以中国东北旺业甸林场为研究区,采用观测时相同步、地理坐标匹配的GF-1 PMS、GF-2 PMS、GF-1 WFV,以及Landsat-8 OLI卫星传感器数据组成空间尺度观测序列(1、2、4、8、16、30 m),并结合支持向量机(SVM)模型,探讨了区域内5种优势树种遥感识别结果的尺度变化规律及其纹理特征参数的影响,同时检验了基于尺度上推转换影像的树种识别结果差异.结果表明: 影像空间分辨率对区域树种识别结果具有显著影响,其中,研究区森林树种识别的最佳影像分辨率为4 m,当分辨率降低至30 m时,树种识别结果最差.在1～8 m影像分辨率范围内,增加纹理信息能够显著提高不同优势树种的识别精度,使总分类精度提升了2.0%～3.6%,但纹理信息对16～30 m影像的识别结果没有显著影响.与真实尺度卫星影像相比,基于升尺度转换影像的树种识别结果及其尺度响应特征存在显著差异,表明在面向多个空间尺度的遥感观测和应用研究中,需要采用真实分辨率影像以确保结果的准确性.     

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.     

Linking canopy leaf area and light environments with tree size distributions to explain Amazon forest demography

Forest biophysical structure – the arrangement and frequency of leaves and stems – emerges from growth, mortality and space filling dynamics, and may also influence those dynamics by structuring light environments. To investigate this interaction, we developed models that could use LiDAR remote sensing to link leaf area profiles with tree size distributions, comparing models which did not (metabolic scaling theory) and did allow light to influence this link. We found that a light environment‐to‐structure link was necessary to accurately simulate tree size distributions and canopy structure in two contrasting Amazon forests. Partitioning leaf area profiles into size‐class components, we found that demographic rates were related to variation in light absorption, with mortality increasing relative to growth in higher light, consistent with a light environment feedback to size distributions. Combining LiDAR with models linking forest structure and demography offers a high‐throughput approach to advance theory and investigate climate‐relevant tropical forest change.     

The Use of Geographic Information Systems, Remote Sensing, and Suitability Modeling to Identify Conifer Restoration Sites with High Biological Potential for Anadromous Fish at the Cedar River Municipal Watershed in Western Washington, U.S.A.

We developed a methodology integrating several forms of remotely sensed data into a Geographic Information Systems (GIS) model that identifies suitable sites for riparian conifer restoration at the Cedar River Municipal Watershed in western Washington, U.S.A. The model integrates vegetative and geomorphic variables with information on the habitat preferences of anadromous fishes to identify riparian stands where conifer restoration would have the greatest biological benefit for salmon recovery. The high-resolution raster datasets used in our analysis were capable of characterizing the biophysical attributes of riparian areas at finer spatial scales than was previously possible. This model is intended to serve as a screening tool to identify candidate sites for riparian area restoration. The assessment approach described in this study can be applied not only to model salmonid habitat at the watershed scale but also to assess landscape patterns relevant to a wide range of restoration goals. This methodological framework offers several advantages over other approaches to restoration site selection and planning. First, the fine-scale spatial resolution of the GIS datasets (pixels ≤5 m) used in the model provides a more accurate representation of the habitat conditions than has been possible with coarser-scale data (pixels ≥5 m). Therefore, the accuracy of site identification is greatly improved. Second, the quantitative nature of the model exercises greater objectivity than some other landscape-scale planning approaches. This regional planning tool could be replicated in other watersheds with comparable datasets and could be applied to identify habitat restoration sites for other species or guilds of species by simply altering the model criteria to match the habitat needs of the target organisms.     

西双版纳地区主要森林植被乔木多样性的时间变化

为了评估云南省西双版纳森林植被乔木多样性的时间变化,该研究通过样方调查收集了该地区4种主要森林植被(热带雨林、热带季节性湿润林、热带山地常绿阔叶林和暖热性针叶林)乔木多样性数据,结合遥感影像提取了该地区4种森林植被在1992年、2000年、2009年和2016年4个时期的分布,用Simpson、Shannon-Wiener和Scaling物种多样性指数对比4种森林植被乔木均匀度差异,并利用Scaling生态多样性指数和灰色关联评价模型,评估该地区在4个时期的森林乔木多样性的时间变化。结果表明:(1)森林面积比例变化有先减少后增加的趋势,表现为由1992年的65.5%减少至2000年的53.42%,减少到2009年的52.49%,再增至2016年的54.73%,但热带雨林呈现持续减少的趋势。(2) 4种森林植被对乔木多样性的贡献有明显差异,均匀度排序是热带雨林>热带山地(低山)常绿阔叶林>暖热性针叶林>热带季节性湿润林,丰富度排序是热带雨林>热带山地(低山)常绿阔叶林>热带季节性湿润林>暖热性针叶林,对乔木多样性贡献的排序是热带雨林>热带山地(低山)常绿阔叶林>热带季节性湿润林>暖热性针叶林。(3)热带雨林和热带季节性湿润林乔木多样性呈现持续减少趋势,4个时期西双版纳森林植被乔木多样性排序为1992年>2009年> 2016年> 2000年。以上结果表明经济活动是影响西双版纳生物多样性的重要原因,保护热带雨林对维持该地区生物多样性具有重要意义。     

Ensemble modeling to predict habitat suitability for a large‐scale disturbance specialist

To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black‐backed Woodpeckers (Picoides arcticus; a burned‐forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D² models) and various combinations of four environmental variables describing burn severity, the north–south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled “ensemble predictions,” consisting of the number of models (0–8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., “no‐analogue”) environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black‐backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned‐forest landscapes where black‐backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large‐scale disturbance specialists.     

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.     

Assessing the suitable habitat for reintroduction of brown trout (Salmo trutta forma fario) in a lowland river: A modeling approach

Huge efforts have been made during the past decades to improve the water quality and to restore the physical habitat of rivers and streams in western Europe. This has led to an improvement in biological water quality and an increase in fish stocks in many countries. However, several rheophilic fish species such as brown trout are still categorized as vulnerable in lowland streams in Flanders (Belgium). In order to support cost‐efficient restoration programs, habitat suitability modeling can be used. In this study, we developed an ensemble of habitat suitability models using metaheuristic algorithms to explore the importance of a large number of environmental variables, including chemical, physical, and hydromorphological characteristics to determine the suitable habitat for reintroduction of brown trout in the Zwalm River basin (Flanders, Belgium), which is included in the Habitats Directive. Mean stream velocity, water temperature, hiding opportunities, and presence of pools or riffles were identified as the most important variables determining the habitat suitability. Brown trout mainly preferred streams with a relatively high mean reach stream velocity (0.2–1 m/s), a low water temperature (7–15°C), and the presence of pools. The ensemble of models indicated that most of the tributaries and headwaters were suitable for the species. Synthesis and applications. Our results indicate that this modeling approach can be used to support river management, not only for brown trout but also for other species in similar geographical regions. Specifically for the Zwalm River basin, future restoration of the physical habitat, removal of the remaining migration barriers and the development of suitable spawning grounds could promote the successful restoration of brown trout.     

大兴安岭南段马鹿生境适宜性分析与生态廊道构建

构建生态廊道在缓解生境破碎化对生物多样性的影响、维持濒危物种的遗传多样性、维护自然生态系统结构完整与功能稳定方面具有重要作用。以内蒙古大兴安岭南段分布的马鹿(Cervus elaphus)种群为研究对象,利用MaxEnt模型对其生境适宜性进行分析,并利用最小累积阻力模型构建潜在生态扩散廊道,探讨大兴安岭南段区域隔离马鹿种群的栖息地连通方案。结果显示,马鹿栖息地呈破碎化状态,种群有明显的隔离分布趋势,现有适宜栖息地具有海拔较低(800—1200 m)、坡度较缓(<15°)、靠近水源、植被类型多为靠近山林的灌丛或草地等特点。所构建12条生态廊道具有经过河流浅水节段、远离村落等特点,便于落实栖息地生态恢复管理措施。研究从区域尺度综合分析了大兴安岭南段马鹿栖息地现状及连通性,有助于优化适宜栖息地格局,促进马鹿扩散和栖息地连通,为该物种隔离种群及其栖息地保护规划提供现实指导和基础资料。     

Developing and testing a habitat suitability index model for Korean water deer (Hydropotes inermis argyropus) and its potential for landscape management decisions in Korea

Geographic information system (GIS) and landscape-level data offer a new opportunity for modeling and evaluating the quality of wildlife habitats. Models of habitat quality have not been developed for some species, and existing models could be improved by incorporating updated information on wildlife–habitat relationships and habitat variables. We developed a GIS-based habitat suitability index (HSI) model for the Korean water deer (Hydropotes inermis argyropus), which often causes human–wildlife conflicts in the Chungnam Province of Korea because of industrialization and urbanization. The model is based on logistic regression analysis, which addresses the impact of multiple habitat variables, such as habitat components, topographic characteristics, and human disturbances. The model yielded a p-value of .289 (χ²?=?9.672) and 65.4% correct prediction level with the overall observation–prediction comparison data. The model demonstrated that a large portion of the province (61.6%) could be regarded as a poor habitat (mean HSI value of the province?=?0.22), while the current habitats of the province could be considered of moderate quality (mean HSI value?=?0.31). In addition, the chance of observation of the deer increases as the HSI level increases, which means that the model yields a good predictive power. Lastly, we used the model to produce a habitat suitability map. Our HSI model enabled us to quantify habitat preferences, which could be the basis for decision-making on habitat protection, mitigation, and enhancement of the Korean water deer. The proposed model is also applicable for improving and enhancing the existing management practices, as well as for establishing an effective wildlife protection policy.     

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.     

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.     

Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality

Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests. In this study, we adopted a new approach to constrain rooting depth in terrestrial ecosystem models over the Amazon using satellite data [moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI)] and Biome-BGC terrestrial ecosystem model. We simulated seasonal variations in gross primary production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1–3 m) is sufficient in regions with a short dry season (e.g. 0–2 months), and deeper roots are required in regions with a longer dry season (e.g. 3–5 and 5–10 m for the regions with 3–4 and 5–6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth.