Using Bayesian networks to incorporate uncertainty in habitat suitability index models

Habitat suitability index (HSI) models rarely characterize the uncertainty associated with their estimates of habitat quality despite the fact that uncertainty can have important management implications. The purpose of this paper was to explore the use of Bayesian belief networks (BBNs) for representing and propagating 3 types of uncertainty in HSI models—uncertainty in the suitability index relationships, the parameters of the HSI equation, and measurement of habitat variables (i.e., model inputs). I constructed a BBN–HSI model, based on an existing HSI model, using Netica™ software. I parameterized the BBN's conditional probability tables via Monte Carlo methods, and developed a discretization scheme that met specifications for numerical error. I applied the model to both real and dummy sites in order to demonstrate the utility of the BBN–HSI model for 1) determining whether sites with different habitat types had statistically significant differences in HSI, and 2) making decisions based on rules that reflect different attitudes toward risk—maximum expected value, maximin, and maximax. I also examined effects of uncertainty in the habitat variables on the model's output. Some sites with different habitat types had different values for E[HSI], the expected value of HSI, but habitat suitability was not significantly different based on the overlap of 90% confidence intervals for E[HSI]. The different decision rules resulted in different rankings of sites, and hence, different decisions based on risk. As measurement uncertainty in habitat variables increased, sites with significantly different (α = 0.1) E[HSI] became statistically more similar. Incorporating uncertainty in HSI models enables explicit consideration of risk and more robust habitat management decisions. © 2012 The Wildlife Society.     

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.     

Landscape Habitat Suitability Index Software

ABSTRACT Habitat suitability index (HSI) models are traditionally used to evaluate habitat quality for wildlife at a local scale. Rarely have such models incorporated spatial relationships of habitat components. We introduce Landscape HSImodels, a new Microsoft Windows® (Microsoft, Redmond, WA)—based program that incorporates local habitat as well as landscape-scale attributes to evaluate habitats for 21 species of wildlife. Models for additional species can be constructed using the generic model option. At a landscape scale, attributes include edge effects, patch area, distance to resources, and habitat composition. A moving window approach is used to evaluate habitat composition and interspersion within areas typical of home ranges and territories or larger. The software and sample data are available free of charge from the United States Forest Service, Northern Research Station at http:www.nrs.fs.fed.ushsi .     

Bird Response to Clear Cutting in Missouri Ozark Forests

Abstract: We evaluated changes in breeding bird density and shifts in territory distribution with respect to clear cutting and timber stand improvement (TSI) of even-aged stands on >300 ha experimental management units as part of the Missouri Ozark Forest Ecosystem Project. After one harvest entry, clear cutting had positive effects on density of indigo bunting (Passerina cyanea), prairie warbler (Dendroica discolor), and yellow-breasted chat (Icteria virens) and negative effects on density of Acadian flycatcher (Empidonax virescens), ovenbird (Seiurus aurocapilla), and worm-eating warbler (Helmitheros vermivorus). In buffer regions within 100 m of clearcuts, indigo bunting, hooded warbler (Wilsonia citrina), wood thrush (Hylocichila mustelina), and Kentucky warbler (Oporornis formosus) densities increased and ovenbird density decreased. Breeding bird densities did not change in interior regions > 100 m from clearcuts except for a small increase for wood thrush. Breeding Acadian flycatcher and ovenbird showed greater use of stands not treated with TSI. We recommend combining adjoining stands to keep clearcut sizes between 8 ha and 13 ha to reduce negative effects on ovenbirds by cutting. We suggest a 7-year offset between the timing of clear cutting and TSI to reduce their combined effects on ovenbird.     

基于生境适宜性指数模型的俚岛海黍子生境层级分布

为了深入了解海黍子生境,利用模型对山东俚岛海黍子生境进行适宜性分析,分别选取温度、盐度、水深、浊度、底质、无机氮浓度、磷酸盐浓度和距海藻床距离8种环境因子,通过层次分析法赋值因子权重,结合空间分析方法建立了海黍子HSI模型。利用该模型对山东俚岛近岸海域2018年春、秋两季的环境因子调查结果进行了海黍子生境分析。结果表明: 研究区域内的海黍子海藻床区域主要由极佳生境和适宜生境组成,春季和秋季的分布面积分别占14.2%和18.6%。海黍子生境层级分布随季节而变化,且不同季节的生境层级具有一定的空间重合性。温度和磷酸盐浓度的适宜性变化具有明显的季节性差异,是导致俚岛海黍子HSI季节变化的主要原因。海黍子HSI模型不仅可用于检测海黍子海藻床区域的生境层级分布,还能发现海黍子潜在的适宜生境区域。这为今后开展海黍子资源保护和人工增殖工作提供了科学参考。     

Habitat suitability and nest survival of white-headed woodpeckers in unburned forests of Oregon

We evaluated habitat suitability and nest survival of breeding white-headed woodpeckers (Picoides albolarvatus) in unburned forests of central Oregon, USA. Daily nest-survival rate was positively related to maximum daily temperature during the nest interval and to density of large-diameter trees surrounding the nest tree. We developed a niche-based habitat suitability model (partitioned Mahalanobis distance) for nesting white-headed woodpeckers using remotely sensed data. Along with low elevation, high density of large trees, and low slope, our habitat suitability model suggested that interspersion–juxtaposition of low- and high-canopy cover ponderosa pine (Pinus ponderosa) patches was important for nest-site suitability. Cross-validation suggested the model performed adequately for management planning at a scale >1 ha. Evaluation of mapped habitat suitability index (HSI) suggested that the maximum predictive gain (HSI = 0.36), where the number of nest locations are maximized in the smallest proportion of the modeled landscape, provided an objective initial threshold for identification of suitable habitat. However, managers can choose the threshold HSI most appropriate for their purposes (e.g., locating regions of low–moderate suitability that have potential for habitat restoration). Consequently, our habitat suitability model may be useful for managing dry coniferous forests for white-headed woodpeckers in central Oregon; however, model validation is necessary before our model could be applied to other locations. © 2011 The Wildlife Society.     

Relationships between survival and habitat suitability of semi‐aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche‐based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi‐aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30‐m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent‐predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability‐fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed.     

基于核密度估计的动物生境适宜度制图方法

生境适宜度制图能提供动物适宜生境的空间分布信息,对野生动物种群管理、保护地规划等非常重要。生境适宜度制图的关键是构建生境适宜度模型(habitat suitability model, HSM),只基于动物出现位置数据构建HSM的方法在实践中得到了非常广泛的应用。然而现有的只基于动物出现位置数据构建HSM的方法还不能很好地直接表达动物生境适宜度和环境因子之间具有生态学意义的数量关系,因此也就不能很好地体现环境因子对动物生境利用的生态学作用。 本文提出了一种基于核密度估计构建HSM的方法,在地理信息系统技术支持下,通过运用核密度估计从代表性的动物出现位置数据中估计出动物出现对各个环境因子的概率密度函数来直接表达生境适宜度与各个环境因子之间的数量关系,以体现环境因子对动物生境利用的生态学作用,在此基础上对生境适宜度与各个环境因子之间的数量关系进行综合构建了具有明确生态学意义的HSM用于动物生境适宜度制图。以美国Voyageures国家公园的白尾鹿(Odocoileus virginianus)生境适宜度制图为例,基于365个出现位置点位数据并结合积雪深度、地表覆被类型、森林边界长度和坡度等环境因子数据,开展了该方法的案例研究。通过交叉验证计算连续Boyce指数对制图结果进行评价,结果表明：基于核密度估计方法构建的HSM预测能力强,所得出的生境适宜度图经10次交叉验证,连续Boyce指数平均值为0.75,标准差为0.11,达到了较高精度。此外,由于基于核密度估计的方法能以“生境适宜度和环境因子之间具有生态学意义的数量关系”的形式来直接体现环境因子对动物生境利用的生态学作用,就模型的可解释性而言,该方法要优于现有的其他构建HSM的方法。     

Using Bobcat Habitat Suitability to Prioritize Habitat Preservation on a Suburbanizing Barrier Island

ABSTRACT Many land-trust organizations attempt to preserve habitat that will benefit specific wildlife species or suites of species. With limited resources available, these organizations need tools to prioritize preservation efforts. One such organization, the Kiawah Island Natural Habitat Conservancy (KINHC), is attempting to preserve wildlife habitat in the face of ever-increasing property values and development pressure on Kiawah Island, South Carolina, USA. We modified an existing bobcat (Lynx rufus) habitat suitability index model, which focuses on suitability of habitats for food, by including components for concealment cover and den habitat. We developed a windows-based computer program that calculates modified habitat suitability index (MHSI) values that can easily be imported into a Geographic Information System for display in map form, allowing for frequent reevaluation of site-specific habitat suitability as land-cover patterns change. We used locations collected from radiocollared bobcats to assess validity of the food and cover components of the MHSI. Bobcats used areas identified as highly suitable for food more than expected during nocturnal time periods (G₅₂ = 640.9, P < 0.001) and areas identified as highly suitable for cover more than expected during diurnal time periods (G₃₇ = 1,194.0, P < 0.001). Our approach for evaluating bobcat habitat suitability will allow KINHC to identify parcels that likely provide the greatest ecological benefit to bobcats and their associated wildlife community. Our approach could be altered to consider habitat requirements of other species, or multiple species, at virtually any location for which fine-scale land-cover data are available.     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

An assessment of human impacts on endangered red pandas (Ailurus fulgens) living in the Himalaya

Anthropogenic factors play an important role in shaping the distribution of wildlife species and their habitats, and understanding the influence of human activities on endangered species can be key to improving conservation efforts as well as the implementation of national strategies for sustainable development. Here, we used species distribution modeling to assess human impacts on the endangered red panda (Ailurus fulgens) in high‐altitude regions of Nepal. We found that the distance to paths (tracks used by people and animals), livestock density, human population density, and annual mean temperature were the most important factors determining the habitat suitability for red pandas in Nepal. This is the first study that attempts to use comprehensive environmental and anthropogenic variables to predict habitat suitability for the red pandas at a national level. The suitable habitat identified by this study is important and could serve as a baseline for the development of conservation strategies for the red panda in Nepal.     

浙江清凉峰国家级自然保护区华南梅花鹿栖息地适宜性评价

理解环境因子对物种空间分布的影响,评价栖息地适宜性现状并预测潜在分布区,对野生动物的管理和保护具有重要意义。华南梅花鹿（Cervus pseudaxis）属国家I级重点保护野生动物,现仅分布于安徽、浙江、江西等狭小片区内。浙江清凉峰自然保护区千顷塘区域是华南梅花鹿的重要分布区,但其面积较小,严重限制了华南梅花鹿的种群发展,亟需对千顷塘及周边区域的栖息地质量进行评价,为华南梅花鹿的保护和野外放归提供科学依据。本研究利用红外相机监测千顷塘区域华南梅花鹿的分布,结合遥感等技术手段获得地形、植被、水源以及人为干扰等8种环境因子,利用MaxEnt构建华南梅花鹿栖息地适宜性模型,对以千顷塘为中心50 km×50 km的范围进行栖息地适宜性评价。研究结果表明,华南梅花鹿倾向于选择海拔1050-1240 m范围内,距道路100-900 m和距人口聚居区3200-3800 m的相对平缓地带。千顷塘区域华南梅花鹿栖息地保护较好,适宜栖息地面积为2224 hm2,占该区域39.1%。千顷塘周边适宜性较高的区域主要为位于其西南部约10 km的山区,该区域为华南梅花鹿提供了3253 hm2的潜在适宜栖息地。建议降低保护区千顷塘区域内的人为干扰,并在其西南部山区尝试开展圈养种群的野外放归工作,以促进其种群发展。     

基于MAXENT模型的贺兰山西坡啮齿动物生境适宜性评价

贺兰山因其拥有独特的植物垂直分布带而十分适宜啮齿动物生存,但自保护区生态恢复以来并未见有研究评价啮齿动物在贺兰山的生境适宜性,使得其分布现状未知。使用GIS技术和MAXENT模型对内蒙古贺兰山国家级自然保护区6种主要啮齿动物进行生境适宜性状况评价及预测,探究啮齿动物在贺兰山的分布现状。结果表明：影响6种啮齿动物的主要环境因子为海拔、坡度和距矿区距离,海拔越高、坡度越大及距矿区距离越近均使啮齿动物生存适宜性降低;两两鼠种生境适宜面积叠加发现,大林姬鼠和阿拉善黄鼠适宜生境重叠面积最大(261.37 km~2),短尾仓鼠和子午沙鼠的适宜生境重叠面积最小(19.00 km~2);6种主要鼠种均适宜的生境面积交集仅有17.14 km~2,占贺兰山总面积的0.47%,6种主要鼠种均不适宜的生境面积有2985.23 km~2,占贺兰山总面积的81.21%。研究表明,啮齿动物栖息地距矿区距离仍是影响其适宜生境的重要因素之一,建议相关部门加强对废弃矿区采取措施,改善保护区啮齿动物生境质量。     

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.     

基于生态位模型的艾比湖国家级自然保护区马鹿生境评价

生境评价和预测是对濒危物种进行有效保护的基础。通过2013年9月和2014年10月对新疆艾比湖国家级自然保护区开展2次秋季野外调查共收集了92处马鹿(Cervuselaphus)出现数据,利用马鹿出现数据作为分布点数据,选取地形、植被类型和气候因子3类23种因子作为生境变量,利用MAXENT生态位模型分析了新疆艾比湖国家级自然保护区马鹿秋季生境适宜性分布特征和主要生境因子对马鹿分布的影响。结果表明:模型预测结果较高,平均AUC(area under the curve,受试工作者曲线下面值)值为0.976;Jackknife检验结果显示:最热月最高温度对马鹿生境分布的影响较大。植被类型和坡度对马鹿生境分布的影响不大。海拔、年降雨量、气温日较差和最热季平均温度是影响马鹿生境分布的主要生境因子。马鹿秋季生境划分为高适宜、次适宜、低适宜和不适宜4个等级,马鹿的高适宜生境区主要分布在研究区域的北部,次适宜及低适宜生境区则分布于高适宜生境区的边缘,而不适宜生境区主要集中在西部和东部地区。研究不仅提供了马鹿在艾比湖的实际分布状况,也为马鹿生境和生境因子的关系方面提供了一个重要的科学依据。     

Territory occupancy by common loons in response to disturbance,habitat, and intraspecific relationships

Structure and distribution of animal territories are driven by a variety of environmental and demographic factors. A peninsular population of common loons (Gavia immer) nests on lakes in northwestern Montana, but does not occupy all apparently suitable breeding territories, suggesting unexplained limitations on population growth. To evaluate territorial dynamics of breeding loons in Montana, we created and tested occupancy models that evaluated the hypothesized effects of disturbance, habitat, and intraspecific relationships on territory occupancy by common loons in Montana from 2003 to 2007. Model-averaged results indicated that the abundance of feeding lakes within 10 km (i.e., forage quality) and the number of territorial pairs within 10 km (i.e., density of loons) were equally supported and related to probabilities of occupancy. We found substantial support that the population was in a state of equilibrium, with the numbers of occupied territories stable in time, but not space. We also found that density of territorial pairs was related to the likelihood that an existing territory would be abandoned, but did not influence the establishment of new territories, suggesting the presence of territorial pairs could be a stronger indicator of territory quality to loons than physical lake characteristics. Our index of human disturbance was not well-supported compared to other factors. Our results suggest management for stable or growing loon populations could be achieved using long-term monitoring and protection of occupied territorial lakes and nearby feeding lakes, because these factors most influenced the probability of occupancy of surrounding lakes. © 2011 The Wildlife Society.     

亚洲象生境长时序变化及其对人象冲突的影响——基于融合MaxEnt与HSI模型的生境适宜性评价方法

生境变化直接关系到物种种群维持与人类安全,揭示其长期变化规律及其对人类的影响,可为物种保护与生境恢复提供科学支撑。但因受物种活动点数据获取与位置精度的局限,鲜见濒危、危险物种的长时序生境变化研究。以人象冲突频发的西双版纳勐海—普洱澜沧地区亚洲象种群(勐海—澜沧象群)活动区为例,提出融合MaxEnt与HSI模型的亚洲象长时序生境适宜性评价方法,即基于荟萃分析筛选出的15个亚洲象生境评价因子,结合近期有限的物种活动点监测数据,利用MaxEnt得到生境评价因子的贡献率,再运用HSI模型计算生境适宜性指数;利用该方法制作出研究区1988—2020年逐年时序的亚洲象生境适宜性图,以分析亚洲象生境的时空变化,将其与亚洲象肇事数据结合,进而分析人象冲突与生境变化的关联。结果表明：(1)基于物种生境偏好不变的前提,融合MaxEnt模型与HSI模型的生境适宜性评价方法可应用于物种的长时序生境评价,且基于亚洲象活动点数据从动物对生境利用的生态学视角定量获取亚洲象对各生境评价因子的偏好程度,使生境评价结果具有良好的生态可解释性;(2)目前亚洲象适宜生境面积占研究区面积三分之一(4039.76 km^...     

Why less complexity produces better forecasts: an independent data evaluation of kelp habitat models

Understanding how species are distributed in the environment is increasingly important for natural resource management, particularly for keystone and habitat – forming species, and those of conservation concern. Habitat suitability models are fundamental to developing this understanding; however their use in management continues to be limited due to often‐vague model objectives and inadequate evaluation methods. Along the Northeast Pacific coast, canopy kelps (Macrocystis pyrifera and Nereocystis luetkeana) provide biogenic habitat and considerable primary production to nearshore ecosystems. We investigated the distribution of these species by examining a series of increasingly complex habitat suitability models ranging from process‐based models based on species’ ecology to complex generalised additive models applied to purpose‐collected survey data. Seeking empirical limits to model complexity, we explored the relationship between model complexity and forecast skill, measured using both cross‐validation and independent data evaluation. Our analysis confirmed the importance of predictors used in models of coastal kelp distributions developed elsewhere (i.e. depth, bottom type, bottom slope, and exposure); it also identified additional important factors including salinity, and potential interactions between exposure and salinity, and slope and tidal energy. Comparative results showed how cross‐validation can lead to over‐fitting, while independent data evaluation clearly identified the appropriate model complexity for generating habitat forecasts. Our results also illustrate that, depending on the evaluation data, predictions from simpler models can out‐perform those from more complex models. Collectively, the insights from evaluating multiple models with multiple data sets contribute to the holistic assessment of model forecast skill. The continued development of methods and metrics for evaluating model forecasts with independent data, and the explicit consideration of model objectives and assumptions, promise to increase the utility of model forecasts to decision makers.     

马鞍列岛褐菖鲉Sebasticus marmoratus栖息地适宜性评价

为了评估趋礁鱼类在岛礁海域的生境适宜度,选取马鞍列岛的褐菖鲉(Sebasticus marmoratus)为指示物种,以2009年获取的水深、盐度、叶绿素a、浊度和底质数据作为褐菖鲉春、冬季栖息地指示因子,建立栖息地适宜度曲线,并计算各站点的栖息地适宜性指数(HSI)。结果显示:1)绿华、花鸟、嵊山沿岸站点HSI普遍较低,枸杞岛、三横山、东库山沿岸站点褐菖鲉HSI相对较高,其中最大值1.0出现在枸杞岛沿岸的站点;2)春季褐菖鲉幼鱼的适宜水深在6 m左右,成鱼适宜在8—12 m的水深处生存;冬季褐菖鲉对8—12 m的水深适宜性良好;3)春季所有褐菖鲉的适宜盐度为30PSU,冬季幼鱼的适宜盐度为27—31PSU,成鱼的适宜盐度为27PSU、31PSU;4)随着叶绿素a和浊度值的增大,褐菖鲉适宜性逐渐降低。底质类型为岩时最适合褐菖鲉生存。5)相关分析显示,褐菖鲉丰度与底质类型相关性最大,而与叶绿素a、浊度呈显著负相关。研究结果表明,初级生产力和浑浊程度越高对褐菖鲉丰度抑制越明显。底质类型是褐菖鲉丰度分布的重要影响因子,其中分布有较多大型海藻的岩礁生境是其最适宜的栖息地。利用2010年春、冬季环境调查和渔获数据进行HSI模型验证,资源丰度随HSI值升高而增加,因此构建的模型可用于趋礁鱼类在岛礁海域的栖息地适宜性分析。