Assessing the potential suitability of forest stands as Kirengeshoma koreana habitat using MaxEnt

The objective of this study was to assess the potential suitability of forest stands of the Mt. Baek-un region in South Korea as habitats for Kirengeshoma koreana by determining essential biotic and abiotic environmental factors using MaxEnt. Presence data were collected from 16 plots in the study area, and a forest stand map was used to assess the potential suitability of the plot as habitat for the species. The topographic site characteristics were analyzed using GIS, and the terrain relief conditions were measured using the topographic position index. The site environmental factors that significantly influenced the potential suitability of the forest stands as habitats for K. koreana were chosen using MaxEnt. The results indicated that landform types, soil depth, and water and light availability at the forest floor were the factors that most strongly influenced K. koreana habitat suitability. These significant environmental factors were assessed to determine the forest stand sites that were most suitable as potential habitats for the species in the study area.     

Revisiting probabilistic neural networks: a comparative study with support vector machines and the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus)

Probabilistic Neural Networks (PNNs) and Support Vector Machines (SVMs) are flexible classification techniques suited to render trustworthy species distribution and habitat suitability models. Although several alternatives to improve PNNs' reliability and performance and/or to reduce computational costs exist, PNNs are currently not well recognised as SVMs because the SVMs were compared with standard PNNs. To rule out this idea, the microhabitat suitability for the Eastern Iberian chub (Squalius valentinus Doadrio & Carmona, 2006) was modelled with SVMs and four types of PNNs (homoscedastic, heteroscedastic, cluster and enhanced PNNs); all of them optimised with Differential Evolution. The fitness function and several performance criteria (correctly classified instances, true skill statistic, specificity and sensitivity) and partial dependence plots were used to assess respectively the performance and reliability of each habitat suitability model. Heteroscedastic and enhanced PNNs achieved the highest performance in every index but specificity. However, these two PNNs rendered ecologically unreliable partial dependence plots. Conversely, homoscedastic and cluster PNNs rendered ecologically reliable partial dependence plots. Thus, Eastern Iberian chub proved to be a eurytopic species, presenting the highest suitability in microhabitats with cover present, low flow velocity (approx. 0.3 m/s), intermediate depth (approx. 0.6 m) and fine gravel (64–256 mm). PNNs outperformed SVMs; thus, based on the results of the cluster PNN, which also showed high values of the performance criteria, we would advocate a combination of approaches (e.g., cluster & heteroscedastic or cluster & enhanced PNNs) to balance the trade-off between accuracy and reliability of habitat suitability models.     

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。     

基于MaxEnt模式的白头叶猴栖息地评价——以广西崇左白头叶猴保护区为例

针对物种分布格局与其环境变量关系的研究,对于生态廊道规划与环境恢复研究具有重要意义.本文以白头叶猴(Trachypithecus leucocephalus)为研究对象,针对广西崇左白头叶猴国家级自然保护区51个白头叶猴分布点和11个环境变量数据,利用MaxEnt模型(maximum entropy modeling)...     

Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species

Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.     

Predicting the habitat usage of African black rhinoceros (Diceros bicornis) using random forest models

Species distribution models are often used in ecology to ascertain relationships between environmental variables and species presence. Modelling to understand this relationship can be used to aid conservation management strategies. In this paper, we applied the random forest classification method to predict habitat used by black rhino for browsing. The random forest model was created using detailed habitat data collected from Ol Pejeta Conservancy in Kenya. Variables from plots where rhino had been present were compared to those not used by rhino. Independent data were used to test the predictive accuracy of the rules generated. The model performed well with the independent test data, correctly classifying 69% of the sampling plots where black rhino were present. Important habitat features that affected rhino presence were browse availability and density of vegetation, with Vachellia drepanolobium (formerly Acacia) and Euclea divinorum being important components. The analysis also highlighted areas of potential high browse pressure, which should be the focus of continued monitoring and management.     

Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic

A predictive understanding of the environmental controls on forest distributions is essential for the conservation of biodiversity and management of landscapes in the tropics. This is particularly true now because of potentially rapid climate change. The floristic complexity of tropical forests and the lack or absence of data severely limits the applicability of modelling methods based on the ecology or distribution of individual species. Here we present an artificial neural network (ANN) model using the information available in the humid tropics of North Queensland: a structural classification of forest types, maps of the forest mosaic, and estimates of spatial environmental variables. The ANN model characterizes the relative suitabilities of environments for 15 forest classes defined by their physiognomy and canopy structure. Inputs include seven climate variables, nine soil parent-material classes, and seven terrain variables. The data used to train the model consisted of a stratified random sample of 75000 points. Output of the model is used to measure the dissimilarity between the environment at each location and the environment that would be most suitable for the forest type that is mapped there. The model is highly successful at distinguishing the relative suitability of environments for the forest classes with 75% of the region's forest mosaic accurately predicted by the model at a one hectare resolution. In contrast, a comparable maximum likelihood classification has an accuracy of only 38%. In the remaining 25% of the region the environments are quite dissimilar to what would be expected for the forest types present there. This is especially the case at boundaries between forest classes and for a transitional forest class. Areas mapped as this disturbed, transitional class are generally classified by the model as having environments suitable to the forest type they are most likely to become. The approach has high potential for the analysis of climate change impacts as well as inferring vegetation patterns in the past and should be applicable wherever vegetation maps and spatial estimates of climate variables are available.     

Jaguar Panthera onca Habitat Modeling in Landscapes Facing High Land‐use Transformation Pressure—Findings from Mato Grosso,Brazil

The modeling of top predators' habitats and the understanding of their environmental requirements in landscapes facing high land‐use transformation pressure have long‐standing importance for the development of conservation strategies. Multi‐distance spatial cluster analysis and logistic regression with environmental weighting for pseudo‐absence designation were applied to understand spatial patterns of jaguar occurrence in Mato Grosso state (Central Western Brazil). This location has been under intense deforestation pressure since the 1970s and is historically one of the most important jaguar habitats in the world. By using a model of five independent variables, we were able to achieve a 73.2 percent success rate of case/non‐case classification and indicate not only a general loss of habitat suitability, but also an increasing interruption of potential migration corridors in the state. Our analysis on a regional scale demonstrates the importance of forest and savannah woodland for jaguar habitat maintenance in the Mato Grosso state. The jaguar species demonstrates a sensitivity to landscape fragmentation, which can be parameterized for improved model building by metrics such as edge density and patch size. Comparisons with previous studies in South America show that parameter selection for jaguar habitat modeling is highly scale‐dependent and that habitat suitability in partially transformed landscapes could be maintained if fragmentation is minimized. Recent land‐use transformation, however, has significantly weakened the conservation status of the Pantanal‐Amazon corridor.     

Resistance is futile: effects of landscape features on gene flow of the northern bobwhite

Habitat for the northern bobwhite (Colinus virginianus) has declined and changed drastically in spatial structure throughout the last century. Undoubtedly such changes have impacted bobwhite and may have altered their ability to access available habitat. We investigated whether landscape resistance, geographic distance, or interstate highway barriers were related to dispersal and gene flow of bobwhite in central and southern Illinois. Landscape resistance was determined from two empirically informed models depicting habitat suitability for bobwhite. During 2007–2008, hunters submitted bobwhite tissue samples from which we amplified 11 microsatellites. The relationship between individual genetic distances and spatial variables was analyzed with Mantel tests and causal modeling was used to verify the spatial variables influencing gene flow. Genetic distance was correlated with geographic distance but showed no relationship with interstate highway barriers. Habitat suitability did not enhance gene flow, and was inversely related in some partial Mantel tests. We suggest that bobwhite dispersal from suitable habitat patches may be less frequent than from suboptimal habitats. Bobwhite may be able to access suitable habitat across gaps of unsuitable habitat but distance limits their dispersal. Because available habitat for bobwhites may have a greater likelihood of being colonized when closer to occupied habitat, we suggest that lands closer to occupied habitat should be targeted for conservation or habitat improvement efforts.     

Foraging efficiency of juvenile bluegill, Lepomis macrochirus, among different vegetated habitats

Optimal foraging theory is devoted to understanding how organisms maximize net energy gain. However, both the theory and empirical studies lack critical components, such as effects of environmental variables across habitats. We addressed the hypothesis that energetic returns of juvenile bluegill are affected by environmental variables characteristic of the vegetated habitats. Predicted optimal diet breadths were calculated and compared to prey items eaten by juvenile bluegill to determine if bluegill were foraging to maximize energetic gain. Differences in habitat profitability among vegetated sites were determined by comparing predictions of maximized energetic return rates (cals^-1) with prey contents of bluegill stomachs. Sizes of most prey items eaten by juvenile bluegill throughout the vegetated sites were smaller than the predicted optimal diet breadths. However, inclusion of smaller prey items in the diet did not seem to affect rate of energetic gain. Energetic return rates were maximized at the 1.5 and 2mm prey size classes and declined only slightly with inclusion of smaller prey sizes. Predicted energetic return rates and average mass in bluegill stomachs were related negatively. Average mass in bluegill stomachs also was associated negatively with Elodea canadensis stem densities and percent of light transfer, suggesting that foraging efficiency of bluegill decreased as plant density and percent of light increased. Results of our research indicate that maximization of energetic return rates is dependent upon availability of prey sizes that contribute to optimal foraging. Thus, determination of those habitats that provide the highest availability of benthic invertebrate prey with the least interference by stems is critical. Enhanced foraging capabilities can promote recruitment, faster growth, better body condition and survival.     

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.     

Multiscale assessment of the influence of habitat structure and composition on bird assemblages in boreal forest

The relative contributions of habitat structure and composition to biodiversity are often scale-dependent. Although bird communities in boreal forest have been largely altered and threatened by forest harvesting, bird habitat selection in this ecosystem has not been fully understood. Our study aimed to assess the relative contributions of habitat structure and composition on the assemblages of boreal birds at multiple spatial scales characterized by radii ranging from 100 to 1,000?m. We recorded bird species occurrence at 96 stations located in an old-growth forest in the C?te-Nord region of Québec, Canada. We characterized habitat structure using the proportion of dense, open, and sparse stands, and habitat composition using the proportions of coniferous, mixedwood, and deciduous stands. We used partial canonical correspondence analyses and hierarchical variance partitioning to assess the relative contribution of habitat structure and composition on bird assemblage, and logistic regression to model the probability of occurrence for individual species in response to habitat variables. Our results revealed that habitat structure and composition explained similar proportions of the variance in bird assemblage (21.7 vs. 21.6?%), regardless of spatial scale. Whilst logistic regression yielded fair predictions in the occurrence of individual species (i.e., area under the receiver-operating characteristic curve >0.70 for 90?% of the species), it further confirmed our findings in community level analysis. Our study indicates that habitat structure and composition are both important in shaping bird assemblages, but spatial scale draws little influence on their relative contributions.     

小兴安岭南麓马鹿冬季适宜生境评价

通过2014-2015年两次冬季野外调查, 将收集的79处马鹿(Cervus elaphus)出现信息作为分布点数据, 选取地形、景观类型、植被特征和人类干扰4类19种因子作为环境变量, 利用最大熵(maximum entropy, MaxEnt)模型, 分析了小兴安岭铁力林业局辖区马鹿种群冬季潜在适宜生境分布特征和主要环境因子对马鹿分布的影响。结果显示: 模型预测精度较高, 训练集与验证集的平均AUC(area under the curve, 受试工作者曲线下面积)值分别为0.949和0.958; Jackknife检验结果表明: 景观类型因子对马鹿生境选择的影响较大; 坡向、距大路距离、距混交林距离、距灌草地距离和距农田距离是影响马鹿生境分布的主要环境因子, 其综合贡献值依次为27.8%、23.9%、19.5%、15.3%和10.4%; 距小路距离对马鹿分布影响较小。我们依据MaxEnt模型最大约登指数, 找到最佳中断点0.22作为阈值将马鹿冬季栖息地划分为适宜和不适宜两个等级, 其面积分别为663.49 km²和1,378.85 km², 分别占研究区总面积的32%和68%。马鹿的适宜生境主要分布在铁力林业局辖区的东部山地和中部林地等区域; 南部地区接近铁力市区, 人类活动频繁, 不适宜马鹿栖息。对马鹿种群的保护管理措施提出3点建议: 控制人为干扰; 构建多样性景观; 优先保护马鹿的潜在适宜生境分布区。     

Predicted Deep-Sea Coral Habitat Suitability for the U.S. West Coast

Regional scale habitat suitability models provide finer scale resolution and more focused predictions of where organisms may occur. Previous modelling approaches have focused primarily on local and/or global scales, while regional scale models have been relatively few. In this study, regional scale predictive habitat models are presented for deep-sea corals for the U.S. West Coast (California, Oregon and Washington). Model results are intended to aid in future research or mapping efforts and to assess potential coral habitat suitability both within and outside existing bottom trawl closures (i.e. Essential Fish Habitat (EFH)) and identify suitable habitat within U.S. National Marine Sanctuaries (NMS). Deep-sea coral habitat suitability was modelled at 500 m×500 m spatial resolution using a range of physical, chemical and environmental variables known or thought to influence the distribution of deep-sea corals. Using a spatial partitioning cross-validation approach, maximum entropy models identified slope, temperature, salinity and depth as important predictors for most deep-sea coral taxa. Large areas of highly suitable deep-sea coral habitat were predicted both within and outside of existing bottom trawl closures and NMS boundaries. Predicted habitat suitability over regional scales are not currently able to identify coral areas with pin point accuracy and probably overpredict actual coral distribution due to model limitations and unincorporated variables (i.e. data on distribution of hard substrate) that are known to limit their distribution. Predicted habitat results should be used in conjunction with multibeam bathymetry, geological mapping and other tools to guide future research efforts to areas with the highest probability of harboring deep-sea corals. Field validation of predicted habitat is needed to quantify model accuracy, particularly in areas that have not been sampled.     

Tree-based ensembles unveil the microhabitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L.): Introducing XGBoost to eco-informatics

Random Forests (RFs) and Gradient Boosting Machines (GBMs) are popular approaches for habitat suitability modelling in environmental flow assessment. However, both present some limitations theoretically solved by alternative tree-based ensemble techniques (e.g. conditional RFs or oblique RFs). Among them, eXtreme Gradient Boosting machines (XGBoost) has proven to be another promising technique that mixes subroutines developed for RFs and GBMs. To inspect the capabilities of these alternative techniques, RFs and GBMs were compared with: conditional RFs, oblique RFs and XGBoost by modelling, at the micro-scale, the habitat suitability for the invasive bleak (Alburnus alburnus L.) and pumpkinseed (Lepomis gibbosus L.). XGBoost outperformed the other approaches, particularly conditional and oblique RFs, although there were no statistical differences with standard RFs and GBMs. The partial dependence plots highlighted the lacustrine origins of pumpkinseed and the preference for lentic habitats of bleak. However, the latter depicted a larger tolerance for rapid microhabitats found in run-type river segments, which is likely to hinder the management of flow regimes to control its invasion. The difference in the computational burden and, especially, the characteristics of datasets on microhabitat use (low data prevalence and high overlapping between categories) led us to conclude that, in the short term, XGBoost is not destined to replace properly optimised RFs and GBMs in the process of habitat suitability modelling at the micro-scale.     

Environmental and neighbourhood effects on tree fern distributions in a neotropical lowland rain forest

Questions: To what extent are the distributions of tropical rain forest tree ferns (Cyatheaceae) related to environmental variation, and is habitat specialization likely to play a role in their local coexistence? Location: Lowland rain forest at La Selva Biological Station, Costa Rica. Methods: Generalized linear (GLM) and generalized additive (GAM) logistic regression were used to model the incidence of four tree fern species in relation to environmental and neighbourhood variables in 1154 inventory plots regularly distributed across 6 km² of old‐growth forest. Small and large size classes of the two most abundant species were modelled separately to see whether habitat associations change with ontogeny. Results: GLM and GAM model results were similar. All species had significant distributional biases with respect to micro‐habitat. Environmental variables describing soil variation were included in the models most often, followed by topographic and forest structural variables. The distributions of small individuals were more strongly related to environmental variation than those of larger individuals. Significant neighbourhood effects (spatial autocorrelation in intraspecific distributions and non‐random overlaps in the distributions of certain species pairs) were also identified. Overlaps between congeners did not differ from random, but there was a highly significant overlap in the distributions of the two most common species. Conclusions: Our results support the view that habitat specialization is an important determinant of where on the rain forest landscape tree ferns grow, especially for juvenile plants. However, other factors, such as dispersal limitation, may also contribute to their local coexistence.     

Diversity of Eastern North American Ant Communities along Environmental Gradients

Studies of species diversity patterns across regional environmental gradients seldom consider the impact of habitat type on within-site (alpha) and between-site (beta) diversity. This study is designed to identify the influence of habitat type across geographic and environmental space, on local patterns of species richness and regional turnover patterns of ant diversity in the northeastern United States. Specifically, I aim to 1) compare local species richness in paired open and forested transects and identify the environmental variables that best correlate with richness; and 2) document patterns of beta diversity throughout the region in both open and forested habitat. I systematically sampled ants at 67 sites from May to August 2010, spanning 10 degrees of latitude, and 1000 meters of elevation. Patterns of alpha and beta diversity across the region and along environmental gradients differed between forested and open habitats. Local species richness was higher in the low elevation and warmest sites and was always higher in open habitat than in forest habitat transects. Richness decreased as temperature decreased or elevation increased. Forested transects show strong patterns of decreasing dissimilarity in species composition between sites along the temperature gradient but open habitat transects did not. Maximum temperature of the warmest month better predicted species richness than either latitude or elevation. I find that using environmental variables as key predictors of richness yields more biologically relevant results, and produces simpler macroecological models than commonly used models which use only latitude and elevation as predictors of richness and diversity patterns. This study contributes to the understanding of mechanisms that structure the communities of important terrestrial arthropods which are likely to be influenced by climatic change.     

An integrated method to create habitat suitability models for fragmented landscapes

Given the pervasive influence of human induced habitat fragmentation in ecological processes, landscape models are a welcome advance. The development of GIS software has allowed a greater use of these models and of analyses of the relationship between species and habitat variables. Habitat suitability models are thus theoretical concepts that can be used for planning in fragmented landscapes and habitat conservation. The most commonly used models are based on single species and on the assignment of suitability values for some environmental variables. Generally the cartographic basis for modeling suitability are thematic maps produced by a Boolean logic. In this paper we propose a model based on a set of focal species and on maps produced by a fuzzy classification method. Focal species, selected by an expert-based approach, provide a practical way of extending the scope of habitat suitability models to the conservation of biodiversity at landscape scale. The utilisation of a classification method that applies a continuity criterion may allow more consideration of the connectivity of an area because it allows a better detection of ecological gradients within a landscape. We applied this methodology to the Tuscany region focusing on terrestrial mammals. Performing a fuzzy classification we produced five land cover maps and through image processing operations we obtained a suitability model which applies a continuity criterion. The resulting suitability fuzzy model seems better for the study of connectivity and fragmentation, especially in areas with high spatial complexity.     

基于MAXENT模型的古田山保护区黑麂生境适宜性评价

浙江开化古田山国家级自然保护区是黑麂（Muntiacus crinifrons）的集中分布区之一。近年来,保护区内黑麂面临着生境丧失和破碎化的威胁。本研究应用MAXENT模型,结合古田山保护区2014-2017年的红外相机监测数据和主要环境变量数据,对保护区内黑麂生境适宜性的季节变化特征及影响因素进行了评价与分析。结果表明：距阔叶林距离、海拔两个变量对黑麂生境适宜性的季节性变化影响最为显著。古田山保护区不同季节黑麂的适宜生境面积为：春季2086.38 hm²、夏季2608.74 hm²、秋季2502.27 hm²和冬季1746.27 hm²,分别占保护区总面积的25.74%、32.18%、30.87%和21.54%。从空间分布来看,黑麂适宜生境主要分布在保护区的核心区和北部区域。建议加强对保护区的核心区和北部区域自然植被的保护与恢复,以及对保护区人为干扰活动的监督和管理。