东北虎豹国家公园东部的野猪生境利用和活动节律初步研究

野猪是世界上分布最广的大型哺乳动物之一,也是东北虎豹国家公园内主要的有蹄类动物,东北虎主要的猎物之一。本文采集了中国虎豹观测网络架设在东北虎豹国家公园东部区域的红外相机完整一年的拍摄数据（2015年5月至2016年4月）,通过占域模型和核密度分析,获得不同季节的野猪生境利用情况和日活动节律,探究人为因素、环境因素和生物因素对野猪生境利用的影响。结果显示,野猪偏好于针叶林和针阔混交林,在靠近居民点的区域活动增加。不同物种间的相互作用对野猪的生境利用产生不同影响,受捕食者压力野猪会倾向于躲避东北虎,但东北豹对野猪占域显示为正向影响。梅花鹿和狍对野猪的影响在不同季节呈现相反,梅花鹿对野猪的占域在冬季呈负向影响,而狍对野猪的占域则在夏季呈负向影响。本研究区域内的野猪夏季有2个活动高峰,冬季有1个活动高峰,在不同季节都倾向于白天和晨昏活动,在日落前达到活动最高峰。本研究揭示东北虎豹国家公园东部区域的野猪生境利用和活动节律是受到植被生境、大型食肉动物和人类因素等多种因素相互作用,长期相适应的结果。     

大气硫循环模型研究进展

本文系统总结了世界范围内各种典型大气硫循环模型的基本内容及适用条件,重点介绍了经典大气硫循环模型和以Oslo CTM2为基础的独立硫循环模型以及与ECHAM、LMDZ、CSIRO、MASINGAR、RCA2、NICAM和MIROC环流模式耦合得到的大气硫循环模型。虽然目前在国际范围内研究所得的大气硫循环模型种类较多,但在模型的研究过程中仍存在缺乏相关数据支撑、缺乏综合考虑化学、辐射和动力学等过程以及缺乏跨学科综合集成研究的问题。针对这些问题,本文提出了优化大气硫排放数据和观测数据的质量、建立共享数据库系统、制订相关数据库使用规定、在研究模型过程中加强学科间合作和资源共享、建立全面的高分辨率大气硫循环模型等建议。     

浙江九龙山国家级自然保护区六种兽类占域分析及其影响因素

我国是世界上兽类多样性最高的国家之一，也是濒危兽类分布的大国。了解濒危兽类在保护区内的分布及影响因素，将有助于开展科学有效的兽类多样性保护和提高保护区的管理效率。然而现有的研究较多集中于自然保护区内的物种编目，探讨多个物种在保护区内的空间分布格局及其影响因素的研究还相对缺乏。本研究以九龙山国家级自然保护区中6种濒危兽类为研究对象，基于占域模型评估这些物种的空间分布及影响因素，以期为后续的监测和保护提供科学依据。结果表明，藏酋猴(Macaca thibetana)平均占域率最高(48.62%)，豹猫(Prionailurus bengalensis)平均占域率最低(2.14%)；豹猫的平均探测率最高(22.95%)，藏酋猴探测率最低(1.75%)。其中，海拔是影响猕猴(Macaca mulatta)、中华鬣羚(Capricornis milneedwardsii)和豹猫占域率的重要因素，而100 m内是否有水源则是影响黑麂(Muntiacus crinifrons)、中华鬣羚和豹猫探测率的重要因素。本研究表明，海拔是影响九龙山地区濒危兽类分布的重要环境因素，而水源则是影响监测效率的重要环...     

繁殖期黄喉雉鹑在景观尺度上的生境选择

生境选择研究是野生动物保护工作中的一项重要内容。黄喉雉鹑（Tetraophasis szechenyii）是我国I级保护动物,先前对其生境选择进行过一定研究,但缺乏对生境选择层次性的考虑和界定。研究遵循学界对生境选择层次性的定义,从景观尺度（第2级尺度）调查四川省雅江县帕姆岭黄喉雉鹑在繁殖期的生境选择。通过设置80个样点,运用录音回放技术调查样点250 m缓冲半径内黄喉雉鹑的占域情况,然后通过地形数据和解译卫片提取有关生境变量,使用逻辑斯蒂回归模型预测影响占域的重要因素。结果显示,冷红杉林、高山栎丛和高山松林的面积占比是决定黄喉雉鹑占域与否的最重要因子。冷红杉林和高山栎丛的占比高、高山松林的占比低,则黄喉雉鹑占域可能性大。该生境选择形式可能与逃避天敌和有利于觅食相关,建议在未来保护管理中注重对冷红杉林和高山栎丛的优先保护。同时,也需对生境的镶嵌式结构进行保护。     

六盘山华北豹的栖息地利用及保护建议

大型食肉动物对维持生态系统的结构和功能具有重要作用, 但大部分大型食肉动物处在持续的种群数量和分布面积下降之中, 面临着急迫的研究与保护需求。华北豹(Panthera pardus japonensis)是我国特有的豹亚种, 也是部分区域森林生态系统中仅存的大型食肉动物, 面临着生境破碎化等威胁。本研究使用红外相机调查了宁夏六盘山国家级自然保护区华北豹的分布, 通过构建占域模型分析了华北豹的栖息地利用, 预测了华北豹的适宜栖息地, 并评估了其生境破碎化格局。研究发现, 华北豹在六盘山的平均占域率约为0.135。华北豹偏好植被发育成熟、地势崎岖、温度较低、远离农田和公路的栖息地, 对于农田边缘和居民点等人类活动区域未显示出显著回避。研究识别的六盘山华北豹适宜栖息地主要沿六盘山东西两侧山脉分布, 55%的适宜栖息地斑块位于六盘山国家级自然保护区内。栖息地斑块面积平均为16 km², 最大达214 km², 约77%的栖息地斑块面积在10 km²以下。研究表明六盘山国家级自然保护区有效地保护了华北豹现有的适宜栖息地, 但仍存在栖息地破碎化和人类活动干扰等关键限制因素。建议通过栖息地改造、人类活动管理等方式增强六盘山华北豹适宜栖息地斑块连通性; 并通过推动华北豹跨省保护工作等举措促进华北豹种群扩散恢复。     

面源污染物输出系数模型的研究进展

面源污染的量化研究对水质管理具有重要意义。经典的输出系数模型法以其结构简单、应用方便、输入数据容易获取等特点在农业面源污染研究中得到了广泛的应用和发展。本研究介绍了输出系数模型法的发展历程及其在农业面源污染负荷估算中的应用,概括了输出系数的获取途径及其在不同条件下的取值,并指出尺度效应问题、输出系数模型与水文模型的耦合将是输出系数模型今后的主要研究方向。     

农业政策环境扩展模型在我国农业面源污染研究中的应用

我国的面源污染问题逐渐受到政府和科学界的重视,然而面源污染是一个复杂的系统过程,涉及多种因素和多个过程。面源(NPS)污染模型作为解决面源污染相关问题的研究和管理工具,在进行面源污染总量估算和严重程度评价、污染物流失路径和影响因素分析、治理策略制定等方面都有重要的作用。在我国,虽然针对面源污染模型进行了大量相关研究,既包含对基于国外模型的应用与验证,也包含基于观测数据自主研发的模型,但仍然存在模型应用和验证案例不足、已有的模型应用同中国面源污染特征结合不足、模型发展同面源污染机理研究结合不足等问题,而农业政策环境扩展(APEX)模型在应对这些问题上具有一定的优势。结合我国面源污染模型相关研究存在的问题、APEX模型模块和研究进展进行了介绍,对APEX模型在我国面源污染相关问题的研究中涉及的畜禽养殖、复杂耕作系统、特定BMP和水稻田的模拟等相关问题的应用前景进行了探讨,以期能够促进我国农业面源污染模型的发展。     

血雉与其捕食者黄喉貂的时空关系初探

猎物与其捕食者间的种间关系对于维持动物群落的结构与功能至关重要。为了解西南地区中高海拔森林生态系统中较常见的血雉（Ithaginis cruentus）及其捕食者黄喉貂（Martes flavigula）之间的相互作用关系,本研究利用2018至2020年在四川栗子坪国家级自然保护区61个红外相机位点的调查数据（累计相机工作日为13 790 d）,量化分析了两者之间的时空关系。在时间维度上,采用核密度函数绘制了血雉（独立有效照片数n = 156）与黄喉貂（n = 98）的日活动节律曲线,分析结果表明,血雉和黄喉貂在日活动高峰上存在明显的回避现象。在空间维度上,单物种占域模型分析结果显示,影响血雉对位点使用的重要因子是海拔和距水源最近距离,影响黄喉貂占域的生境因子主要是坡度;单季节双物种占域模型的结果显示,（1）在物种作用和环境变量的影响下,黄喉貂存在时血雉对位点的占域率显著低于黄喉貂不存在时;（2）随着海拔的升高,黄喉貂与血雉的空间关系呈现出由分离（物种间的互作因子SIF值小于1）转为重合（物种间的互作因子SIF值大于1）的趋势。本文使用日活动模式和占域模型分析二者在时间生态位和空间生态位上的关系,初步揭示了血雉与其捕食者黄喉貂在时空上呈现不完全分化的特点,为深入理解该地区山地森林生态系统中猎物与其捕食者时空分布关系的研究提供了范例与基础信息。     

海洋甲壳类生物资源评估方法研究进展

目前甲壳类生物资源,如蟹、龙虾、对虾及南极磷虾等组成了全球庞大且极具商业价值的渔业.虽然这些渔业的重要性逐步提升,规模也在扩大,但相对于其他渔业,适合且有效的海洋甲壳类资源评估与管理方法仍需进一步发展.本文回顾和评价了各种用于甲壳类生物资源评估的方法与模型,对剩余产量模型、时滞差分模型、损耗模型及体长结构模型等应用到甲壳类生物资源评估的4种主要模型进行了归纳和分析,简要地总结了这几种模型在应用时所需要的假设前提以及对所需数据的要求等,并对比分析了几种模型的优、缺点.此外,本文还列举了关于资源评估方法中模型的假设要求.参数的估算方法、不确定性来源及一般性解决办法等.最后,本文对甲壳类资源评估方法的发展方向和前景进行了展望.     

生态系统服务供需评估方法研究进展

生态系统服务供需评估可为识别地区均衡状况、生态权衡、生态管理、生态补偿提供依据,是生态系统服务研究的热点之一。本文收集与梳理国内外生态系统服务评估方法,将其归纳为生态模型法、价值法、参与法和经验统计模型法四大类,从供需评估角度、适用尺度范围和数据要求等方面,比较了各类方法在生态服务评估中的应用情况及优缺点。四类方法应用以评估服务供给为主,而在需求评估中的应用较少。生态模型法与价值法适用尺度范围广,涵盖从局地尺度到全球尺度,参与法与经验统计模型法则多用于中小尺度。生态模型法所需数据种类多,对数据要求高,评估精度也相对较高,在方法适用尺度、数据获取、推广应用等方面更具优势。未来生态系统服务供需评估方法研究需重点考虑以下三个方面:构建多样、标准供需指标体系,加强生态系统服务评估精确性研究,加强模型集成与开发。     

Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection‐corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present‐day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1‐km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species‐specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost‐effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern‐day data of the species community to analyze global change process.     

基于标记-重捕模型开展野生动物红外相机种群监测的方法及案例

红外相机技术的广泛应用推动了动物种群生态学研究方法的发展和革新, 特别是基于标记-重捕模型框架通过非损伤取样方式对物种数量和密度等种群参数的可靠估计, 为保护濒危物种和评估保护成效提供了有力的科学依据。对于身体上具有独特天然标记的动物(如多数猫科动物), 可依据红外相机拍摄身体上的独特斑点或条纹鉴别个体, 再运用标记-重捕模型, 估计动物种群数量、密度等参数。本文概述了标记-重捕模型的基本原理、特点以及国内外的应用, 特别是近年来发展出的空间标记-重捕模型。总结了从相机布设到数据分析的具体流程、操作原则, 并以青城山家猫为实例, 展示了应用红外相机数据通过空间标记-重捕模型估计种群密度和数量的基本步骤。最后展望了该模型在种群动态、景观廊道设计、资源选择等方面的应用和发展趋势。     

Modelling occurrence and abundance of species when detection is imperfect

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.     

Colonization and extinction dynamics of an annual plant metapopulation in an urban environment

The metapopulation framework considers that the spatiotemporal distribution of organisms results from a balance between the colonization and extinction of populations in a suitable and discrete habitat network. Recent spatially realistic metapopulation models have allowed patch dynamics to be investigated in natural populations but such models have rarely been applied to plants. Using a simple urban fragmented population system in which favourable habitat can be easily mapped, we studied patch dynamics in the annual plant Crepis sancta (Asteraceae). Using stochastic patch occupancy models (SPOMs) and multi‐year occupancy data we dissected extinction and colonization patterns in our system. Overall, our data were consistent with two distinct metapopulation scenarios. A metapopulation (sensu stricto) dynamic in which colonization occurs over a short distance and extinction is lowered by nearby occupied patches (rescue effect) was found in a set of patches close to the city centre, while a propagule rain model in which colonization occurs from a large external population was most consistent with data from other networks. Overall, the study highlights the importance of external seed sources in urban patch dynamics. Our analysis emphasizes the fact that plant distributions are governed not only by habitat properties but also by the intrinsic properties of colonization and dispersal of species. The metapopulation approach provides a valuable tool for understanding how colonization and extinction shape occupancy patterns in highly fragmented plant populations. Finally, this study points to the potential utility of more complex plant metapopulation models than traditionally used for analysing ecological and evolutionary processes in natural metapopulations.     

Long-term demographic surveys reveal a consistent relationship between average occupancy and abundance within local populations of a butterfly metapopulation

Species distribution models are the tool of choice for large-scale population monitoring, environmental association studies and predictions of range shifts under future environmental conditions. Available data and familiarity of the tools rather than the underlying population dynamics often dictate the choice of specific method – especially for the case of presence–absence data. Yet, for predictive purposes, the relationship between occupancy and abundance embodied in the models should reflect the actual population dynamics of the modelled species. To understand the relationship of occupancy and abundance in a heterogeneous landscape at the scale of local populations, we built a spatio-temporal regression model of populations of the Glanville fritillary butterfly Melitaea cinxia in a Baltic Sea archipelago. Our data comprised nineteen years of habitat surveys and snapshot data of land use in the region. We used variance partitioning to quantify relative contributions of land use, habitat quality and metapopulation covariates. The model revealed a consistent and positive, but noisy relationship between average occupancy and mean abundance in local populations. Patterns of abundance were highly variable across years, with large uncorrelated random variation and strong local population stochasticity. In contrast, the spatio-temporal random effect, habitat quality, population connectivity and patch size explained variation in occupancy, vindicating metapopulation theory as the basis for modelling occupancy patterns in fragmented landscapes. Previous abundance was an important predictor in the occupancy model, which points to a spillover of abundance into occupancy dynamics. While occupancy models can successfully model large-scale population structure and average occupancy, extinction probability estimates for local populations derived from occupancy-only models are overconfident, as extinction risk is dependent on actual, not average, abundance.     

Effect of detection heterogeneity in occupancy‐detection models: an experimental test of time‐to‐first‐detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time‐to‐first‐detection (TTD) occupancy models have been proposed as a cost–effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy‐detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala Phascolarctos cinereus populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness‐of‐fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy‐detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness‐of‐fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy‐detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.     

Exploiting opportunistic observations to estimate changes in seasonal site use: An example with wetland birds

Nonsystematically collected, a.k.a. opportunistic, species observations are accumulating at a high rate in biodiversity databases. Occupancy models have arisen as the main tool to reduce effects of limited knowledge about effort in analyses of opportunistic data. These models are generally using long closure periods (e.g., breeding season) for the estimation of probability of detection and occurrence. Here, we use the fact that multiple opportunistic observations in biodiversity databases may be available even within days (e.g., at popular birding localities) to reduce the closure period to 1 day in order to estimate daily occupancies within the breeding season. We use a hierarchical dynamic occupancy model for daily visits to analyze opportunistic observations of 71 species from nine wetlands during 10 years. Our model derives measures of seasonal site use within seasons from estimates of daily occupancy. Comparing results from our “seasonal site use model” to results from a traditional annual occupancy model (using a closure criterion of 2 months or more) showed that our model provides more detailed biologically relevant information. For example, when the aim is to analyze occurrences of breeding species, an annual occupancy model will over‐estimate site use of species with temporary occurrences (e.g., migrants passing by, single itinerary prospecting individuals) as even a single observation during the closure period will be viewed as an occupancy. Alternatively, our model produces estimates of the extent to which sites are actually used. Model validation based on simulated data confirmed that our model is robust to changes and variability in sampling effort and species detectability. We conclude that more information can be gained from opportunistic data with multiple replicates (e.g., several reports per day almost every day) by reducing the time window of the closure criterion to acquire estimates of occupancies within seasons.     

Improving species occupancy estimation when sampling violates the closure assumption

Site occupancy models that account for imperfect detection of species are increasingly utilized in ecological research and wildlife monitoring. Occupancy models require replicate surveys to estimate detection probability over a time period where the occupancy status at sampled sites is assumed closed. Unlike mark–recapture models, few studies have examined how violations of closure can bias occupancy estimates. Our study design allowed us to differentiate among two processes that violate the closure assumption during a sampling season: 1) repeated destructive sampling events that result in either short‐ or long‐term site avoidance by the target species and 2) sampling occurring over a time period during which non‐random movements of the target species result in variable occupancy status. We used dynamic occupancy models to quantify the potential bias in occupancy estimation associated with these processes for a terrestrial salamander system. Our results provide strong evidence of a systematic decrease in salamander occupancy within a field season. Chronic disturbance due to repeated searches of natural cover objects accelerated natural declines in species occurrence on the forest surface as summer progressed. We also observed a strong but temporary disturbance effect on salamander detection probability associated with repeated sampling within a 24‐h. period. We generalized our findings by conducting a simulation to evaluate how violations of closure can bias occupancy estimates when local extinction occurs within a sampling season. Our simulation study revealed general sensitivity of estimates from single‐season occupancy models to violations of closure, with the strength and direction of bias varying between scenarios. Bias was minimal when extinction proba bility or the number of sample occasions was relatively low. Our research highlights the importance of addressing closure in occupancy studies and we provide multiple solutions, using both design‐ and model‐based frameworks, for minimizing bias associated with non‐random changes in occupancy and repeated sampling disturbances.