Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic‐index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random‐forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R² ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random‐forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R² ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.     

Remote sensing: Using the spatial domain

Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification.     

Predicting tree diversity across the United States as a function of modeled gross primary production.

At the regional and continental scale, ecologists have theorized that spatial variation in biodiversity can be interpreted as a response to differences in climate. To test this theory we assumed that ecological constraints associated with current climatic conditions (2000-2004) might best be correlated with tree richness if expressed through satellite-derived measures of gross primary production (GPP), rather than the more commonly used, but less consistently derived, net primary production. To evaluate current patterns in tree diversity across the contiguous United States we acquired information on tree composition from the USDA Forest Service's Forest Inventory and Analysis program that represented more than 17,4000 survey plots. We selected 2693 cells of 1000 km2 within which a sufficient number of plots were available to estimate tree richness per hectare. Our estimates of forest productivity varied from simple vegetation indices indicative of the fraction of light intercepted by canopies at 16-d intervals, a product from the MODIS (Moderate Resolution Imaging Spectro-radiometer), to 8- and 10-d GPP products derived with minimal climatic data (MODIS) and SPOT-Vegetation (Systeme Pour l'Observation de la Terre), to 3-PGS (Physiological Principles Predicting Growth with Satellites), which requires both climate and soil data. Across the contiguous United States, modeled predictions of gross productivity accounted for between 51% and 77% of the recorded spatial variation in tree diversity, which ranged from 2 to 67 species per hectare. When the analyses were concentrated within nine broadly defined ecoregions, predictive relations largely disappeared. Only 3-PGS predictions fit a theorized unimodal function by being able to distinguish highly productive forests in the Pacific Northwest that support lower than expected tree diversity. Other models predicted a continuous steep rise in tree diversity with increasing productivity, and did so with generally better or nearly equal precision with fewer data requirements.     

Spatial connectivity in a large river system: resolving the sources and fate of dissolved organic matter

Large rivers are generally heterogeneous and productive systems that receive important inputs of dissolved organic matter (DOM) from terrestrial and in situ sources. Thus, they are likely to play a significant role in the biogeochemical cycling of the DOM flowing to the oceans. The asymmetric spatial gradient driven by directional flow and environmental heterogeneity contributes to the fate of DOM flowing downstream. Yet, the relative effects of spatial connectivity and environmental heterogeneity on DOM dynamics are poorly understood. For example, since environmental variables show spatial heterogeneity, the variation explained by environmental and spatial variables may be redundant. We used the St. Lawrence River (SLR) as a representative large river to resolve the unique influences of environmental heterogeneity and spatial connectivity on DOM dynamics. We used three-dimensional fluorescence matrices combined with parallel factor analysis (PARAFAC) to characterize the DOM pool in the SLR. Seven fluorophores were modeled, of which two were identified to be of terrestrial origin and three from algal exudates. We measured a set of environmental variables that are known to drive the fate of DOM in aquatic systems. Additionally, we used asymmetric eigenvector map (AEM) modeling to take spatial connectivity into account. The combination of spatial and environmental models explained 85% of the DOM variation. We show that spatial connectivity is an important driver of DOM dynamics, as a large fraction of environmental heterogeneity was attributable to the asymmetric spatial gradient. Along the longitudinal axis, we noted a rapid increase in dissolved organic carbon (DOC), mostly controlled by terrestrial input of DOM originating from the tributaries. Variance partitioning demonstrated that freshly produced protein-like DOM was found to be the preferential substrate for heterotrophic bacteria undergoing rapid proliferation, while humic-like DOM was more correlated to the diffuse attenuation coefficient of UVA radiation.     

Integrated Monitoring and Information Systems for Managing Aquatic Invasive Species in a Changing Climate

Abstract:  Changes in temperature, precipitation, and other climatic drivers and sea-level rise will affect populations of existing native and non-native aquatic species and the vulnerability of aquatic environments to new invasions. Monitoring surveys provide the foundation for assessing the combined effects of climate change and invasions by providing baseline biotic and environmental conditions, although the utility of a survey depends on whether the results are quantitative or qualitative, and other design considerations. The results from a variety of monitoring programs in the United States are available in integrated biological information systems, although many include only non-native species, not native species. Besides including natives, we suggest these systems could be improved through the development of standardized methods that capture habitat and physiological requirements and link regional and national biological databases into distributed Web portals that allow drawing information from multiple sources. Combining the outputs from these biological information systems with environmental data would allow the development of ecological-niche models that predict the potential distribution or abundance of native and non-native species on the basis of current environmental conditions. Environmental projections from climate models can be used in these niche models to project changes in species distributions or abundances under altered climatic conditions and to identify potential high-risk invaders. There are, however, a number of challenges, such as uncertainties associated with projections from climate and niche models and difficulty in integrating data with different temporal and spatial granularity. Even with these uncertainties, integration of biological and environmental information systems, niche models, and climate projections would improve management of aquatic ecosystems under the dual threats of biotic invasions and climate change.     

Extrinsic regime shifts drive abrupt changes in regeneration dynamics at upper treeline in the Rocky Mountains, U.S.A

Given the widespread and often dramatic influence of climate change on terrestrial ecosystems, it is increasingly common for abrupt threshold changes to occur, yet explicitly testing for climate and ecological regime shifts is lacking in climatically sensitive upper treeline ecotones. In this study, quantitative evidence based on empirical data is provided to support the key role of extrinsic, climate-induced thresholds in governing the spatial and temporal patterns of tree establishment in these high-elevation environments. Dendroecological techniques were used to reconstruct a 420-year history of regeneration dynamics within upper treeline ecotones along a latitudinal gradient (approximately 44-35 degrees N) in the Rocky Mountains. Correlation analysis was used to assess the possible influence of minimum and maximum temperature indices and cool-season (November-April) precipitation on regional age-structure data. Regime-shift analysis was used to detect thresholds in tree establishment during the entire period of record (1580-2000), temperature variables significantly Correlated with establishment during the 20th century, and cool-season precipitation. Tree establishment was significantly correlated with minimum temperature during the spring (March-May) and cool season. Regime-shift analysis identified an abrupt increase in regional tree establishment in 1950 (1950-1954 age class). Coincident with this period was a shift toward reduced cool-season precipitation. The alignment of these climate conditions apparently triggered an abrupt increase in establishment that was unprecedented during the period of record. Two main findings emerge from this research that underscore the critical role of climate in governing regeneration dynamics within upper treeline ecotones. (1) Regional climate variability is capable of exceeding bioclimatic thresholds, thereby initiating synchronous and abrupt changes in the spatial and temporal patterns of tree establishment at broad regional scales. (2) The importance of climate parameters exceeding critical threshold values and triggering a regime shift in tree establishment appears to be contingent on the alignment of favorable temperature and moisture regimes. This research suggests that threshold changes in the climate system can fundamentally alter regeneration dynamics within upper treeline ecotones and, through the use of regime-shift analysis, reveals important climate-vegetation linkages.     

Landscape-level influences of terrestrial snake occupancy within the southeastern United States

Habitat loss and degradation are thought to be the primary drivers of species extirpations, but for many species we have little information regarding specific habitats that influence occupancy. Snakes are of conservation concern throughout North America, but effective management and conservation are hindered by a lack of basic natural history information and the small number of large-scale studies designed to assess general population trends. To address this information gap, we compiled detection/nondetection data for 13 large terrestrial species from 449 traps located across the southeastern United States, and we characterized the land cover surrounding each trap at multiple spatial scales (250-, 500-, and 1000-m buffers). We used occupancy modeling, while accounting for heterogeneity in detection probability, to identify habitat variables that were influential in determining the presence of a particular species. We evaluated 12 competing models for each species, representing various hypotheses pertaining to important habitat features for terrestrial snakes. Overall, considerable interspecific variation existed in important habitat variables and relevant spatial scales. For example, kingsnakes (Lampropeltis getula) were negatively associated with evergreen forests, whereas Louisiana pinesnake (Pituophis ruthveni) occupancy increased with increasing coverage of this forest type. Some species were positively associated with grassland and scrub/shrub (e.g., Slowinski's cornsnake, Elaphe slowinskii) whereas others, (e.g., copperhead, Agkistrodon contortrix, and eastern diamond-backed rattlesnake, Crotalus adamanteus) were positively associated with forested habitats. Although the species that we studied may persist in varied landscapes other than those we identified as important, our data were collected in relatively undeveloped areas. Thus, our findings may be relevant when generating conservation plans or restoration goals. Maintaining or restoring landscapes that are most consistent with the ancestral habitat preferences of terrestrial snake assemblages will require a diverse habitat matrix over large spatial scales.     

Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing.

Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.     

Climate change,risk management and the end of Nomadic pastoralism

Mobility has been argued to be the single factor explaining why some pastoralists do relatively well during extreme climatic events, while others do not, because mobility works by taking advantage of the spatial and temporal structure of resource failure by moving away from scarcity towards abundance. In spite of this, a common governmental management strategy is to resettle pastoral populations and thereby significantly reduce mobility. By revealing the underlying logic of mobility for Tibetan pastoralists, this paper questions official policy that aims at privatizing communally owned rangelands since it reduces pastoral flexibility and access to key resources. This is especially pertinent in the face of climate change. While little is known as to the specifics of how climate change will affect nomadic pastoralists, environmental variability is likely to increase. Consequently, policies resulting in decreased mobility may exacerbate the negative effects of climate change because of a positive feedback between climate and negative density dependence.     

Climate and predation dominate juvenile and adult recruitment in a turtle with temperature-dependent sex determination

Conditions experienced early in life can influence phenotypes in ecologically important ways, as exemplified by organisms with environmental sex determination. For organisms with temperature-dependent sex determination (TSD), variation in nest temperatures induces phenotypic variation that could impact population growth rates. In environments that vary over space and time, how does this variation influence key demographic parameters (cohort sex ratio and hatchling recruitment) in early life stages of populations exhibiting TSD? We leverage a 17-year data set on a population of painted turtles, Chrysemys picta, to investigate how spatial variation in nest vegetation cover and temporal variation in climate influence early life-history demography. We found that spatial variation in nest cover strongly influenced nest temperature and sex ratio, but was not correlated with clutch size, nest predation, total nest failure, or hatching success. Temporal variation in climate influenced percentage of total nest failure and cohort sex ratio, but not depredation rate, mean clutch size, or mean hatching success. Total hatchling recruitment in a year was influenced primarily by temporal variation in climate-independent factors, number of nests constructed, and depredation rate. Recruitment of female hatchlings was determined by stochastic variation in nest depredation and annual climate and also by the total nest production. Overall population demography depends more strongly on annual variation in climate and predation than it does on the intricacies of nest-specific biology. Finally, we demonstrate that recruitment of female hatchlings translates into recruitment of breeding females into the population, thus linking climate (and other) effects on early life stages to adult demographics.     

Mapping shifts in spatial synchrony in grassland birds to inform conservation planning

Spatial synchrony, defined as the correlated fluctuations in abundance of spatially separated populations, can be caused by regional fluctuations in natural and anthropogenic environmental population drivers. Investigations into the geography of synchrony can provide useful insight to inform conservation planning efforts by revealing regions of common population drivers and metapopulation extinction vulnerability. We examined the geography of spatial synchrony and decadal changes in these patterns for grassland birds in the United States and Canada, which are experiencing widespread and persistent population declines. We used Bayesian hierarchical models and over 50 years of abundance data from the North American Breeding Bird Survey to generate population indices within a 2° latitude by 2° longitude grid. We computed and mapped mean local spatial synchrony for each cell (mean detrended correlation of the index among neighboring cells), along with associated uncertainty, for 19 species in 2, 26-year periods, 1968–1993 and 1994–2019. Grassland birds were predicted to increase in spatial synchrony where agricultural intensification, climate change, or interactions between the 2 increased. We found no evidence of an overall increase in synchrony among grassland bird species. However, based on the geography of these changes, there was considerable spatial heterogeneity within species. Averaging across species, we identified clusters of increasing spatial synchrony in the Prairie Pothole and Shortgrass Prairie regions and a region of decreasing spatial synchrony in the eastern United States. Our approach has the potential to inform continental-scale conservation planning by adding an additional layer of relevant information to species status assessments and spatial prioritization of policy and management actions. Our work adds to a growing literature suggesting that global change may result in shifting patterns of spatial synchrony in population dynamics across taxa with broad implications for biodiversity conservation.     

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.     

土地利用、覆被变化（LUCC）与环境变化关系研究进展

土地利用、覆被变化（LUCC）作为环境变化的主要原因之一,已成为全球变化研究的前沿和热点问题。文章总结了国内外 LUCC 与环境变化关系的主要研究成果和方法,继而从气候、碳循环、土壤环境、水环境以及生态环境对土地利用方式的限制等方面概括了 LUCC 与环境变化之间的关系。LUCC 通过改变大气成分和下垫面性质对气候造成影响;影响着陆地生态系统的碳循环;改变土壤的理化性质,带来土壤污染、土壤养分迁移等土壤质量问题;并且引起水体的非点源污染,影响区域的产水量和水循环。同时,环境变化对LUCC具有限制作用。不仅通过特定的气候环境直接限制土地的利用方式;还间接通过借助人类生态环境意识的改变,实现对区域土地利用强度与方式的约束。LUCC既是全球环境变化的原因,也是全球环境变化的结果。LUCC 与生态环境之间存在着复杂的、非线性的动态反馈关系。进一步探讨了当前 LUCC 与环境变化关系的主要研究方向和相关研究方法,针对目前存在的缺乏统一的指标体系,研究区域、时空尺度单一,以单要素静态研究为主,实验研究相对薄弱以及动态模拟不够等问题,提出加强跨学科综合交叉研究、注重多尺度探讨LUCC的环境效应、构建一个 LUCC 环境效应研究的统一指标体系及加强“3S”技术与模拟模型的融合等建议。为寻求更科学更合理的土地利用方式提供了基础信息。     

Connectivity Planning to Address Climate Change

As the climate changes, human land use may impede species from tracking areas with suitable climates. Maintaining connectivity between areas of different temperatures could allow organisms to move along temperature gradients and allow species to continue to occupy the same temperature space as the climate warms. We used a coarse‐filter approach to identify broad corridors for movement between areas where human influence is low while simultaneously routing the corridors along present‐day spatial gradients of temperature. We modified a cost–distance algorithm to model these corridors and tested the model with data on current land‐use and climate patterns in the Pacific Northwest of the United States. The resulting maps identified a network of patches and corridors across which species may move as climates change. The corridors are likely to be robust to uncertainty in the magnitude and direction of future climate change because they are derived from gradients and land‐use patterns. The assumptions we applied in our model simplified the stability of temperature gradients and species responses to climate change and land use, but the model is flexible enough to be tailored to specific regions by incorporating other climate variables or movement costs. When used at appropriate resolutions, our approach may be of value to local, regional, and continental conservation initiatives seeking to promote species movements in a changing climate. Planificación de Conectividad para Atender el Cambio Climático     

蓝藻水华强度的显著相关环境因素识别模型

为识别蓝藻水华强度的显著相关环境因素,克服现有研究中因变量选择不合理、时间与空间精度较低等问题,构建了以蓝藻水华强度等级为因变量,以水质、水文和气象3类监测指标为自变量的多元线性回归模型,并将该模型应用于太湖蓝藻水华研究.基于水华面积和集聚强度数据,用7级量表生成水华强度等级值,使因变量具有宏观性,避免了仅使用叶绿素a浓度等类似指标表示水华强度所体现出的微观性不足.该数据集的时间精度达到每天采样2次,空间精度则达到太湖湖湾内的某个水域空间范围.因此因变量具有适度宏观性,而自变量的值则与因变量的值在较高的时间和空间精度基础上严格对应.模型的分析结果显示,太湖大贡山水域蓝藻水华强度与气温和硝酸盐浓度呈显著正相关,与风速、湿度和电导率呈显著负相关.上述结论与该研究领域的主流结论一致,验证了该模型的有效性.     

Nonparametric spatial regression of survival probability: visualization of population sinks in Eurasian woodcock

Both evolutionary ecologists and wildlife managers make inference based on how fitness and demography vary in space. Spatial variation in survival can be difficult to assess in the wild because (1) multisite study designs are not well suited to populations that are continuously distributed across a large area and (2) available statistical models accounting for detectability less than 1.0 do not easily cope with geographical coordinates. Here we use penalized splines within a Bayesian state-space modeling framework to estimate and visualize survival probability in two dimensions. The approach is flexible in that no parametric form for the relationship between survival and coordinates need be specified a priori. To illustrate our method, we study a game species, the Eurasian Woodcock Scolopax rusticola, based on band recovery data (5000 individuals) collected over a > 50 000-km2 area in west-central France with contrasted habitats and hunting pressures. We find that spatial variation in survival probability matches an index of hunting pressure and creates a mosaic of population sources and sinks. Such analyses could provide guidance concerning the spatial management of hunting intensity or could be used to identify pathways of spatial variation in fitness, for example, to study adaptation to changing landscape and climate.     

Stochastic population dynamics in populations of western terrestrial garter snakes with divergent life histories

Comparative evaluations of population dynamics in species with temporal and spatial variation in life-history traits are rare because they require long-term demographic time series from multiple populations. We present such an analysis using demographic data collected during the interval 1978-1996 for six populations of western terrestrial garter snakes (Thamnophis elegans) from two evolutionarily divergent ecotypes. Three replicate populations from a slow-living ecotype, found in mountain meadows of northeastern California, were characterized by individuals that develop slowly, mature late, reproduce infrequently with small reproductive effort, and live longer than individuals of three populations of a fast-living ecotype found at lakeshore locales. We constructed matrix population models for each of the populations based on 8-13 years of data per population and analyzed both deterministic dynamics based on mean annual vital rates and stochastic dynamics incorporating annual variation in vital rates. (1) Contributions of highly variable vital rates to fitness (lambda(s)) were buffered against the negative effects of stochastic variation, and this relationship was consistent with differences between the meadow (M-slow) and lakeshore (L-fast) ecotypes. (2) Annual variation in the proportion of gravid females had the greatest negative effect among all vital rates on lambda(s). The magnitude of variation in the proportion of gravid females and its effect on lambda(s) was greater in M-slow than L-fast populations. (3) Variation in the proportion of gravid females, in turn, depended on annual variation in prey availability, and its effect on lambda(s) was 4 23 times greater in M-slow than L-fast populations. In addition to differences in stochastic dynamics between ecotypes, we also found higher mean mortality rates across all age classes in the L-fast populations. Our results suggest that both deterministic and stochastic selective forces have affected the evolution of divergent life-history traits in the two ecotypes, which, in turn, affect population dynamics. M-slow populations have evolved life-history traits that buffer fitness against direct effects of variation in reproduction and that spread lifetime reproduction across a greater number of reproductive bouts. These results highlight the importance of long-term demographic and environmental monitoring and of incorporating temporal dynamics into empirical studies of life-history evolution.     

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.     

稳定氢氧同位素在定量区分植物水分利用来源中的应用

全球气候变化下陆地生态系统的适应性是当前科学研究关注的主题之一,了解生态系统如何响应及影响全球气候变化有利于人类对未来生存环境的预测和适应。生态系统中不同来源水分对植物生长相对贡献决的大小一定程度上决定了生态系统对气候变化的响应方式、程度和响应结果,因此跟踪和分析植物利用水分的来源是制定全球气候变化对策的一个重要研究内容。本文介绍了稳定氢氧同位素技术研究历史及其在定量区分植物利用水分的来源研究中的应用原理与具体方法。由于土壤水分在被植物根系吸收及随后沿导管向上传输的过程中,与外界环境不发生水分交换,因此不存在同位素的分馏过程,所以植物茎木质部水分同位素组成能反映出植物利用的来源水分同位素信息。通过比较植物茎木质部水分与植物利用的不同来源水分同位素值,利用二项或三项分隔线性混合模型(two-orthree-compartment linear mixing model),可以估算出植物对不同来源水分的相对使用量。而由于植物叶片水分同位素组成受到周围环境的温度、湿度、降雨和土壤水分的异质性等许多因素的影响,通过比较分析植物茎木质部水分和叶片水分同位素组成的差异可以得到植物周围环境的气候信息。植物利用水分的来源存在显著的季节性差异,并且,不同生活型植物在利用水分来源上存在明显不同。植物根系的分布及根深是决定植物利用水分来源的一个重要的因素,表层和深层根系的相对分布及其活性影响着植物吸收水分的范围。当然,利用线型分隔混合模型定量区分植物利用水分的不同来源,还有许多值得改进的地方,而且,尽管稳定同位素技术在植物科学中的应用正迅速发展起来,但利用稳定氢氧同位素来分析环境因素对植物影响的研究还只是刚刚展开,还有许多方面值得去进一步探索。