Joint Effects of Habitat Heterogeneity and Species’ Life-History Traits on Population Dynamics in Spatially Structured Landscapes

Both habitat heterogeneity and species’ life-history traits play important roles in driving population dynamics, yet there is little scientific consensus around the combined effect of these two factors on populations in complex landscapes. Using a spatially explicit agent-based model, we explored how interactions between habitat spatial structure (defined here as the scale of spatial autocorrelation in habitat quality) and species life-history strategies (defined here by species environmental tolerance and movement capacity) affect population dynamics in spatially heterogeneous landscapes. We compared the responses of four hypothetical species with different life-history traits to four landscape scenarios differing in the scale of spatial autocorrelation in habitat quality. The results showed that the population size of all hypothetical species exhibited a substantial increase as the scale of spatial autocorrelation in habitat quality increased, yet the pattern of population increase was shaped by species’ movement capacity. The increasing scale of spatial autocorrelation in habitat quality promoted the resource share of individuals, but had little effect on the mean mortality rate of individuals. Species’ movement capacity also determined the proportion of individuals in high-quality cells as well as the proportion of individuals experiencing competition in response to increased spatial autocorrelation in habitat quality. Positive correlations between the resource share of individuals and the proportion of individuals experiencing competition indicate that large-scale spatial autocorrelation in habitat quality may mask the density-dependent effect on populations through increasing the resource share of individuals, especially for species with low mobility. These findings suggest that low-mobility species may be more sensitive to habitat spatial heterogeneity in spatially structured landscapes. In addition, localized movement in combination with spatial autocorrelation may increase the population size, despite increased density effects.     

Point process models,the dimensions of biodiversity and the importance of small-scale biotic interactions

Aims Recent mechanistic explanations for community assembly focus on the debates surrounding niche-based deterministic and dispersal-based stochastic models. This body of work has emphasized the importance of both habitat filtering and dispersal limitation, and many of these works have utilized the assumption of species spatial independence to simplify the complexity of the spatial modeling in natural communities when given dispersal limitation and/or habitat filtering. One potential drawback of this simplification is that it does not consider species interactions and how they may influence the spatial distribution of species, phylogenetic and functional diversity. Here, we assess the validity of the assumption of species spatial independence using data from a subtropical forest plot in southeastern China.Methods We use the four most commonly employed spatial statistical models—the homogeneous Poisson process representing pure random effect, the heterogeneous Poisson process for the effect of habitat heterogeneity, the homogenous Thomas process for sole dispersal limitation and the heterogeneous Thomas process for joint effect of habitat heterogeneity and dispersal limitation—to investigate the contribution of different mechanisms in shaping the species, phylogenetic and functional structures of communities.Important findings Our evidence from species, phylogenetic and functional diversity demonstrates that the habitat filtering and/or dispersal-based models perform well and the assumption of species spatial independence is relatively valid at larger scales (50×50 m). Conversely, at local scales (10×10 and 20×20 m), the models often fail to predict the species, phylogenetic and functional diversity, suggesting that the assumption of species spatial independence is invalid and that biotic interactions are increasingly important at these spatial scales.     

The importance of fine‐scale predictors of wild boar habitat use in an isolated population

1. Predicting the likelihood of wildlife presence at potential wildlife–livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High‐resolution data can help identify fine‐scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human–wildlife conflict.
2. This study uses fine‐scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi‐likelihood were used to identify habitat‐based and anthropogenic predictors of wild boar signs.
3. Our models showed that within the Forest of Dean, wild boar signs were more likely to be seen in spring, in forest‐type habitats, closer to the center of the forest and near litter bins. In the area surrounding the Forest of Dean, wild boar signs were more likely to be seen in forest‐type habitats and near recreational parks and less likely to be seen near livestock.
4. This approach shows that wild boar habitat use can be predicted using fine‐scale data over comparatively small areas and in human‐dominated landscapes, while taking account of the spatial correlation from other nearby fine‐scale data‐points. The methods we use could be applied to map habitat use of other wildlife species in similar landscapes, or of movement‐restricted, isolated, or fragmented wildlife populations.

     

Variation in behavioral traits of two frugivorous mammals may lead to differential responses to human disturbance

Human activities can lead to a shift in wildlife species’ spatial distribution. Understanding the specific effects of human activities on ranging behavior can improve conservation management of wildlife populations in human‐dominated landscapes. This study evaluated the effects of forest use by humans on the spatial distribution of mammal species with different behavioral adaptations, using sympatric western lowland gorilla and central chimpanzee as focal species. We collected data on great ape nest locations, ecological and physical variables (habitat distribution, permanent rivers, and topographic data), and anthropogenic variables (distance to trails, villages, and a permanent research site). Here, we show that anthropogenic variables are important predictors of the distribution of wild animals. In the resource model, the distribution of gorilla nests was predicted by nesting habitat distribution, while chimpanzee nests were predicted first by elevation followed by nesting habitat distribution. In the anthropogenic model, the major predictors of gorilla nesting changed to human features, while the major predictors of chimpanzee nesting remained elevation and the availability of their preferred nesting habitats. Animal behavioral traits (body size, terrestrial/arboreal, level of specialization/generalization, and competitive inferiority/superiority) may influence the response of mammals to human activities. Our results suggest that chimpanzees may survive in human‐encroached areas whenever the availability of their nesting habitat and preferred fruits can support their population, while a certain level of human activities may threaten gorillas. Consequently, the survival of gorillas in human‐dominated landscapes is more at risk than that of chimpanzees. Replicating our research in other sites should permit a systematic evaluation of the influence of human activity on the distribution of mammal populations. As wild animals are increasingly exposed to human disturbance, understanding the resulting consequences of shifting species distributions due to human disturbance on animal population abundance and their long‐term survival will be of growing conservation importance.     

Habitat use of the ocelot (Leopardus pardalis) in Brazilian Amazon

Amazonia forest plays a major role in providing ecosystem services for human and sanctuaries for wildlife. However, ongoing deforestation and habitat fragmentation in the Brazilian Amazon has threatened both. The ocelot is an ecologically important mesopredator and a potential conservation ambassador species, yet there are no previous studies on its habitat preference and spatial patterns in this biome. From 2010 to 2017, twelve sites were surveyed, totaling 899 camera trap stations, the largest known dataset for this species. Using occupancy modeling incorporating spatial autocorrelation, we assessed habitat use for ocelot populations across the Brazilian Amazon. Our results revealed a positive sigmoidal correlation between remote‐sensing derived metrics of forest cover, disjunct core area density, elevation, distance to roads, distance to settlements and habitat use, and that habitat use by ocelots was negatively associated with slope and distance to river/lake. These findings shed light on the regional scale habitat use of ocelots and indicate important species–habitat relationships, thus providing valuable information for conservation management and land‐use planning.     

Importance of estimating matrix quality for modeling species distribution in complex tropical landscapes: a test with Atlantic forest small mammals

Information to guide decision making is especially urgent in human dominated landscapes in the tropics, where urban and agricultural frontiers are still expanding in an unplanned manner. Nevertheless, most studies that have investigated the influence of landscape structure on species distribution have not considered the heterogeneity of altered habitats of the matrix, which is usually high in human dominated landscapes. Using the distribution of small mammals in forest remnants and in the four main altered habitats in an Atlantic forest landscape, we investigated 1) how explanatory power of models describing species distribution in forest remnants varies between landscape structure variables that do or do not incorporate matrix quality and 2) the importance of spatial scale for analyzing the influence of landscape structure. We used standardized sampling in remnants and altered habitats to generate two indices of habitat quality, corresponding to the abundance and to the occurrence of small mammals. For each remnant, we calculated habitat quantity and connectivity in different spatial scales, considering or not the quality of surrounding habitats. The incorporation of matrix quality increased model explanatory power across all spatial scales for half the species that occurred in the matrix, but only when taking into account the distance between habitat patches (connectivity). These connectivity models were also less affected by spatial scale than habitat quantity models. The few consistent responses to the variation in spatial scales indicate that despite their small size, small mammals perceive landscape features at large spatial scales. Matrix quality index corresponding to species occurrence presented a better or similar performance compared to that of species abundance. Results indicate the importance of the matrix for the dynamics of fragmented landscapes and suggest that relatively simple indices can improve our understanding of species distribution, and could be applied in modeling, monitoring and managing complex tropical landscapes.     

Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint

Anthropogenic landscape change (i.e., disturbance) is recognized as an important factor in the decline and extirpation of wildlife populations. Understanding and monitoring the relationship between wildlife distribution and disturbance is necessary for effective conservation planning. Many studies consider disturbance as a covariate explaining wildlife behavior. However, we propose that there are several advantages to considering the spatial relationship between disturbance and wildlife directly using utilization distributions (UDs), including objective assessment of the spatially explicit overlap between wildlife and disturbance, and the ability to track trends in this relationship over time. Here, we examined how central mountain woodland caribou (Rangifer tarandus caribou) distribution changed over time in relation to (i) anthropogenic disturbance, baseline range (defined using telemetry data from 1998 to 2005), and alpine habitat; and (ii) interannual climate variation (North Pacific Index; NPI). We developed seasonal UDs for caribou in west‐central Alberta and east‐central British Columbia, Canada, monitored with GPS collars between 1998 and 2013. We mapped the cumulative annual density of disturbance features within caribou range and used indices of overlap to determine the spatial relationship and trend between caribou UDs, anthropogenic disturbance, baseline range, alpine habitat, and the NPI. Anthropogenic disturbance increased over time, but the overlap between caribou UDs and disturbance did not. Caribou use of alpine habitat during spring, fall, and late winter increased over time, concurrent with a decrease in use of baseline range. Overlap between caribou UDs and disturbance increased during spring and fall following relatively cold, snowy winters (high NPI), but overall, climate did not explain changes in caribou distribution over time. We provide evidence supporting the hypothesis that caribou populations adjust their spatial distribution in relation to anthropogenic landscape change. Our findings could have implications for population persistence if distributional shifts result in greater use of alpine habitat during winter. Monitoring long‐term changes in the distribution of populations is a valuable component of conservation planning for species at risk in disturbed landscapes.     

Predicting Disease Risk,Identifying Stakeholders,and Informing Control Strategies: A Case Study of Anthrax in Montana

Infectious diseases that affect wildlife and livestock are challenging to manage and can lead to large-scale die-offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high-risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi-species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs.     

A spatial theory for emergent multiple predator–prey interactions in food webs

Predator–prey interaction is inherently spatial because animals move through landscapes to search for and consume food resources and to avoid being consumed by other species. The spatial nature of species interactions necessitates integrating spatial processes into food web theory and evaluating how predators combine to impact their prey. Here, we present a spatial modeling approach that examines emergent multiple predator effects on prey within landscapes. The modeling is inspired by the habitat domain concept derived from empirical synthesis of spatial movement and interactions studies. Because these principles are motivated by synthesis of short‐term experiments, it remains uncertain whether spatial contingency principles hold in dynamical systems. We address this uncertainty by formulating dynamical systems models, guided by core habitat domain principles, to examine long‐term multiple predator–prey spatial dynamics. To describe habitat domains, we use classical niche concepts describing resource utilization distributions, and assume species interactions emerge from the degree of overlap between species. The analytical results generally align with those from empirical synthesis and present a theoretical framework capable of demonstrating multiple predator effects that does not depend on the small spatial or temporal scales typical of mesocosm experiments, and help bridge between empirical experiments and long‐term dynamics in natural systems.     

Identifying Habitat Linkages for American Black Bears in North Carolina,USA

ABSTRACT Understanding landscape structure and the role of habitat linkages is important to managing wildlife populations in fragmented landscapes. We present a data-based method for identifying local- and regional-scale habitat linkages for American black bears (Ursus americanus) on the Albemarle-Pamlico Peninsula of North Carolina, USA. We used weights-of-evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (n = 35 bears). The model included 3 variables measured at a 0.2-km² scale: forest cohesion, forest diversity, and forest-agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10-fold model validation. Forest cohesion and forest-agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4-lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.     

Does Learning or Instinct Shape Habitat Selection?

Habitat selection is an important behavioural process widely studied for its population-level effects. Models of habitat selection are, however, often fit without a mechanistic consideration. Here, we investigated whether patterns in habitat selection result from instinct or learning for a population of grizzly bears (Ursus arctos) in Alberta, Canada. We found that habitat selection and relatedness were positively correlated in female bears during the fall season, with a trend in the spring, but not during any season for males. This suggests that habitat selection is a learned behaviour because males do not participate in parental care: a genetically predetermined behaviour (instinct) would have resulted in habitat selection and relatedness correlations for both sexes. Geographic distance and home range overlap among animals did not alter correlations indicating that dispersal and spatial autocorrelation had little effect on the observed trends. These results suggest that habitat selection in grizzly bears are partly learned from their mothers, which could have implications for the translocation of wildlife to novel environments.     

Random sampling does not exclude spatial dependence: The importance of neutral models for ecological hypothesis testing

Lájer (2007) notes that, to investigate phytosociological and ecological relationships, many authors apply traditional inferential tests to sets of relevés obtained by non-random methods. Unfortunately, this procedure does not provide reliable support for hypothesis testing because non-random sampling violates the assumptions of independence required by many parametric inferential tests. Instead, a random sampling scheme is recommended. Nonetheless, random sampling will not eliminate spatial autocorrelation. For instance, a classical law of geography holds that everything in a piece of (biotic) space is interrelated, but near objects are more related than distant ones. Because most ecological processes that shape community structure and species coexistence are spatially explicit, spatial autocorrelation is a vital part of almost all ecological data. This means that, independently from the underlying sampling design, ecological data are generally spatially autocorrelated, violating the assumption of independence that is generally required by traditional inferential tests. To overcome this drawback, randomization tests may be used. Such tests evaluate statistical significance based on empirical distributions generated from the sample and do not necessarily require data independence. However, as concerns hypothesis testing, randomization tests are not the universal remedy for ecologists, because the choice of inadequate null models can have significant effects on the ecological hypotheses tested. In this paper, I emphasize the need of developing null models for which the statistical assumptions match the underlying biological mechanisms.     

Statistical Modeling of Observational Data With Spatial Dependencies

Abstract I provide a brief introduction to the concept of spatial autocorrelation and its incorporation into regression-type models. Spatial autocorrelation occurs when the response variable is correlated with itself at other locations in the region of interest. The autocorrelation usually takes a specific form where observations close in space are more correlated than those farther apart, and the rate of decay of the correlation is a function of the distance separating 2 locations. I present 2 commonly used models: 1) geostatistical modeling in which data are collected at points in the study region and 2) conditional autoregression (lattice) models in which data are aggregated over small nonoverlapping sub-areas of the study region. I also describe incorporation of explanatory covariates, such as habitat or physico-chemical attributes. I emphasize frequentist methods, but I briefly describe Bayesian approaches. I also provide some advantages, such as obtaining correct standard errors for estimators, and disadvantages, such as requirements for larger sample sizes, of incorporating spatial autocorrelation into the modeling effort. This information can aid researchers in designing and analyzing models of the relationships between species distributions and habitat. As a result, more informative models can be developed which further aid in management of wildlife.     

Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes

Understanding the spatial pattern of species distributions is fundamental in biogeography, and conservation and resource management applications. Most species distribution models (SDMs) require or prefer species presence and absence data for adequate estimation of model parameters. However, observations with unreliable or unreported species absences dominate and limit the implementation of SDMs. Presence-only models generally yield less accurate predictions of species distribution, and make it difficult to incorporate spatial autocorrelation. The availability of large amounts of historical presence records for freshwater fishes of the United States provides an opportunity for deriving reliable absences from data reported as presence-only, when sampling was predominantly community-based. In this study, we used boosted regression trees (BRT), logistic regression, and MaxEnt models to assess the performance of a historical metacommunity database with inferred absences, for modeling fish distributions, investigating the effect of model choice and data properties thereby. With models of the distribution of 76 native, non-game fish species of varied traits and rarity attributes in four river basins across the United States, we show that model accuracy depends on data quality (e.g., sample size, location precision), species’ rarity, statistical modeling technique, and consideration of spatial autocorrelation. The cross-validation area under the receiver-operating-characteristic curve (AUC) tended to be high in the spatial presence-absence models at the highest level of resolution for species with large geographic ranges and small local populations. Prevalence affected training but not validation AUC. The key habitat predictors identified and the fish-habitat relationships evaluated through partial dependence plots corroborated most previous studies. The community-based SDM framework broadens our capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa (e.g., benthic macroinvertebrates, birds) usually observed by community-based sampling designs.     

Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose

Identifying spatial patterns in species diversity represents an essential task to be accounted for when establishing conservation strategies or monitoring programs. Predicting patterns of species richness by a model-based approach has recently been recognised as a significant component of conservation planning. Finding those environmental predictors which are related to these patterns is crucial since they may represent surrogates of biodiversity, indicating in a fast and cheap way the spatial location of biodiversity hotspots and, consequently, where conservation efforts should be addressed. Predictive models based on classical multiple linear regression or generalised linear models crowded the recent ecological literature. However, very often, problems related with spatial autocorrelation in observed data were not adequately considered. Here, a spatially-explicit data-set on birds presence and distribution across the whole Tuscany region was analysed. Species richness was calculated within 1 × 1 km grid cells and 10 environmental predictors (e.g. altitude, habitat diversity and satellite-derived landscape heterogeneity indices) were included in the analysis. Integrating spatial components of variation with predictive ecological factors, i.e. using geostatistical models, a general model of bird species richness was developed and used to obtain predictive regional maps of bird diversity hotspots. A meaningful subset of environmental predictors, namely habitat productivity, habitat heterogeneity, combined with topographic and geographic information, were included in the final geostatistical model. Conservation strategies based on the predicted hotspots as well as directions for increasing sampling effort efficiency could be extrapolated by the proposed model.     

Do species population parameters and landscape characteristics affect the relationship between local population abundance and surrounding habitat amount?

In landscape ecology, correlational approaches are typically used to analyse links between local population abundance, and the surrounding habitat amount to estimate biologically-relevant landscape size (extent) for managing endangered or pest populations. The direction, strength, and spatial extent of the correlations are then sometimes interpreted in terms of species population parameters. Here we simulated the population dynamics of generalized species across spatially explicit landscapes that included two distinct habitat types. We investigated how characteristics of a landscape (structure), including the variation in habitat quality and spatial aggregation of the habitat, and the precise population-dynamic properties of the simulated species (dispersal and growth rates) affect the correlation between population abundance and amount of surrounding favourable habitat in the landscape. To evaluate these spatial extents of correlation, proportions of favourable habitat were calculated within several circles of increasing diameter centred on sampling patches of favourable habitat where population abundance was recorded.We found that the value of the correlation coefficients between population abundance and amount of surrounding favourable habitat depended on both population dynamic parameters and landscape characteristics. Coefficients of correlation increased with the variation in habitat quality and the aggregation of favourable habitat in the landscape, but decreased with the dispersal distance. The distance at which the correlation was maximized was sensitive to an interaction between the level of aggregation of the habitat and the dispersal distance; whereas the greatest distance at which a significant correlation occurred was more sensitive to the variation in habitat quality. Our results corroborate the view that correlational analyses do provide information on the local population dynamics of a species in a given habitat type and on its dispersal rate parameters. However, even in simplified, model frameworks, direct relationships are often difficult to disentangle and global landscape characteristics should be reported in any studies intended to derive population-dynamic parameters from correlations. Where possible, replicated landscapes should be examined in order to control for the interaction between population dynamics and landscape structure. Finally, we recommend using species-specific, population-dynamic modelling in order to interpret correctly the observed patterns of correlation in the landscape.     

Coyotes as Sentinels for Monitoring Bovine Tuberculosis Prevalence in White-Tailed Deer

Abstract: Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB), is endemic in free-ranging white-tailed deer (Odocoileus virginianus) in 5 counties (Alcona, Alpena, Montmorency, Oscoda, and Presque Isle) in the northeastern Lower Peninsula of Michigan, USA. The presence of a wildlife reservoir of tuberculosis in Michigan and the incidence of bTB in cattle (Bos taurus) resulted in Michigan losing its bTB accredited-free status. Subsequent wildlife surveillance programs identified relatively high disease prevalence in coyotes (Canis latrans), generating interest in their potential to serve as a sentinel species to detect bTB prevalence in white-tailed deer. Our goal was to develop an empirical basis for generating hypotheses about the spatial epidemiology of bTB infection in coyotes for future surveillance, management, and modeling efforts. Though variation in coyote home-range size may confound attempts to spatially correlate the incidence of disease in the sentinel and host species at a fine scale, overlap zones (OZs) between adjacent coyote home ranges may be the appropriate sample unit for spatially correlating disease prevalence in coyotes and white-tailed deer. Because overlapping home ranges are generally configured around resource rich (e.g., small mammals and white-tailed deer) timber management patches, the OZ concentrates spatial interaction between adjacent groups in a relatively small area. Furthermore, there is a direct relationship between interaction probabilities and the spatial dispersion of those patches. The latter finding provides a useful metric to incorporate into future efforts to develop spatially explicit models of bTB dynamics. Modeling efforts can then be used as a foundation to predict the epidemiological ramifications of alterations in intensively managed forested landscapes. (JOURNAL OF WILDLIFE MANAGEMENT 71(5):1545-1554; 2007)     

Competing populations on fragmented landscapes with spatially structured heterogeneities: improved landscape generation and mixed dispersal strategies

Interactions between two species competing for space were studied using stochastic spatially explicit lattice-based simulations as well as pair approximations. The two species differed only in their dispersal strategies, which were characterized by the proportion of reproductive effort allocated to long-distance (far) dispersal versus short-distance (near) dispersal to adjacent sites. All population dynamics took place on landscapes with spatially clustered distributions of suitable habitat, described by two parameters specifying the amount and the local spatial autocorrelation of suitable habitat. Whereas previous results indicated that coexistence between pure near and far dispersers was very rare, taking place over only a very small region of the landscape parameter space, when mixed strategies are allowed, multiple strategies can coexist over a much wider variety of landscapes. On such spatially structured landscapes, the populations can partition the habitat according to local conditions, with one species using pure near dispersal to exploit large contiguous patches of suitable habitat, and another species using mixed dispersal to colonize isolated smaller patches (via far dispersal) and then rapidly exploit those patches (via near dispersal). An improved mean-field approximation which incorporates the spatially clustered habitat distribution is developed for modeling a single species on these landscapes, along with an improved Monte Carlo algorithm for generating spatially clustered habitat distributions.      

Metapopulation extinction in fragmented landscapes: using bacteria and protozoa communities as model ecosystems

Extinction is notoriously difficult to study because of the long timescales involved and the difficulty in ascertaining that extinction has actually occurred. The effect of habitat subdivision, or fragmentation, on extinction risk is even harder to study, as it requires copious replication of habitat patches on large spatial scales and control of area effects between treatments. I used simple small-scale communities of bacteria and protozoa to study extinction in response to habitat loss and habitat fragmentation. I studied several different community configurations, each with three trophic levels. Unlike most metapopulation studies (experimental as well as theoretical), which have tended to deal with inherently unstable species interactions, I deliberately used community configurations that were persistent in large stock cultures. I recorded the time to extinction of the top predator in single habitat patches of different sizes and in fragmented systems with different degrees of subdivision but the same amount of available habitat. Habitat loss reduced the time to extinction of isolated populations. Fragmented systems went extinct sooner than corresponding unfragmented (continuous) systems of the same overall size. Unfragmented populations persisted longer than fragmented systems (metapopulations) with or without dispersal corridors between subpopulations. In fact, fragmented systems where the fragments were linked by dispersal corridors went extinctly significantly sooner than those where subpopulations were completely isolated from each other. If these results extend to more "natural" systems, it suggests a need for caution in management programs that emphasize widespread establishment of wildlife corridors in fragmented landscapes.     

同域分布中华鬣羚与中华斑羚时空生态位特征

为了解同域分布有蹄类在环境复杂的山地森林生境中以何种方式维持种间关系以实现稳定共存,基于物种分布模型与日活动模式分析了四川省岷山、邛崃、大相岭、小相岭和凉山五大山系同域分布中华鬣羚(Capricornis milneedwardsii)与中华斑羚(Naemorhedus griseus)的时空生态位特征。结果显示:(1)在四川省五大山系,中华鬣羚的适宜栖息地面积为28006.07 km²,占研究区总面积的26.18%,其中高适宜栖息地面积为10015.90 km²,中华斑羚的适宜栖息地面积为21073.32 km²,占研究区总面积的19.71%,其中高适宜栖息地面积为8396.22 km²;(2)中华鬣羚与中华斑羚在生境因子选择上相似性高、栖息地重叠面积大,其空间生态位重叠度指数D=0.776,I=0.949,其适宜栖息地的主要重叠区域位于岷山和邛崃山系;(3)中华鬣羚与中华斑羚的日活动节律重叠指数为0.812;(4)中华鬣羚与中华斑羚属于同域分布的资源利用型竞争物种,中华鬣羚的存在会显著影响中华斑羚的日活动节律(P=0.016);二者同域分布时都会增加其昼间活动强度,并增加活动高峰期的强度及持续时间。本研究初步分析了中华鬣羚与中华斑羚的时空生态位特征,揭示了二者在空间、时间生态位上种群共存及种间竞争的耦合关系。研究有利于深入理解同域分布动物时空生态位特征、近缘物种的共存机制及种间竞争关系,为有蹄类等珍稀野生动物种群及栖息地的保护提供科学参考。