Diversity in the cork oak forests of the northern Straits of Gibraltar Region (Southern Spain)

Several components of diversity (ecological, taxonomic, chorological and morphological– functional) were studied in the woody vegetation of the cork oak forests in the northern Straits of Gibraltar Region, and their relationship to the two main environmental gradients (one mainly climatic—precipitation, fog—and another secondary related to the disturbance and hydric stress associated with hillslope orientation). An inventory of the woody species and their cover measure was carried out on 94 plots. The relationship between the components of diversity and the main environmental gradients was analysed by means of regression analysis. Decreases of species richness, Shannon diversity index, Western Mediterranean species cover, percentage of endemism and the share of the Herrera Syndrome I in diversity were related to the increase of the precipitation and the fog. However, the cover and percentage of species of the Circum-Mediterranean and Mediterranean-Eurosiberian chorological groups increase with the precipitation and the fog. Disturbance and hydric stress associated with hillslope orientation, have a negative effect on specific richness of the Mediterranean-Eurosiberian chorological group and taxonomic singularity, but its have a positive effect on the share of the Herrera Syndrome I in diversity. The number of edaphic endemisms is associated mainly with disturbance. In the cork forests does not exist an inverse relationship between the number of endemisms and floristic richness, as it happens in other Mediterranean areas. It has suggested that this pattern is due to effect of competitive exclusion associated with disturbance in vegetation on relatively fertile substrate.     

使用大熊猫数据评估Biomod2和MaxEnt分布预测模型的表现

物种分布模型是物种研究和保护者常用的工具.不同模型的预测结果可能相差很大,对研究者选择模型造成一定的难度.本研究使用大熊猫的实际分布数据评估了两种常见物种分布模型Biomod2和最大熵模型(MaxEnt)的表现,运用ROC曲线下面积(area under the curve,AUC)、真实技巧统计值(true skill statistics,TSS)、KAPPA统计量3种指标综合评估了两种模型预测结果的准确度.结果表明: 当使用的物种分布数据和模拟重复次数足够多的时候,两者都能够给出相当准确的预测.相对于MaxEnt,Biomod2的预测准确度更高,尤其是在物种分布点稀少的情况下.然而,Biomod2使用难度较大,运行时间较长,数据处理能力有限.研究者应基于对预测结果的误差要求来选择模型.在误差要求明确且两个模型都能满足误差要求时,建议使用MaxEnt,否则应优先考虑使用Biomod2.     

Understanding the distribution of wood-inhabiting fungi in European beech reserves from species-specific habitat models

We assessed how environmental drivers influence the occurrences of wood-inhabiting macrofungi in European beech forests, using an extensive dataset of fruit body records collected in 53 reserves across twelve European countries. We found that the 105 species included in this study varied greatly in their responses to covariates related to resource quality, climate and forest connectivity, both in the strength and direction of the observed effects. Climate was the most important driver for some species, while others responded more to connectivity, or simply to the presence of high quality substrates within the reserves. Species occurrences varied also across geographical regions, especially between the UK and the rest of Europe. Our results show that wood-inhabiting fungi in European beech forests respond individualistically to habitat filters and differ in their biogeographical distribution patterns, and they thus provide a detailed perspective of how wood-inhabiting fungal communities are structured across Europe.     

Reproductive success of the endangered shrub Buxus balearica Lam. (Buxaceae): pollen limitation, and inbreeding and outbreeding depression

The increasingly common phenomenon of habitat fragmentation raises the probability of pollination failure in a number of species, as both pollen quantity and quality often decrease as populations become isolated. We experimentally investigated whether pollen was limiting reproductive success of the endangered shrub Buxus balearica in five populations, two continental and three insular, during 2002 and 2003. Pollen limitation varied among populations and years, but such variation was not related to density or degree of isolation. All populations showed inbreeding depression at different phases of the reproductive cycle, although its effects differed greatly among sites. Between-population outcrossing did not have a consistent effect on several components of fitness. The highest levels of inbreeding depression – detected at the level of fruit and seed set- occurred at the smallest and least fecund populations from each region. This indicates that further fragmentation of the populations of this already endangered species could certainly threaten their survival.     

Improving predictive mapping in Swiss mire ecosystems through re‐calibration of indicator values

Question: How may Landolt indicator values be re‐calibrated to improve the performance of predictive models? Location: Mires Gross Moos Schwändital (1250 m a.s.l.) in the Prealps, Burgmoos (465 m. a.s.l.) on the Central Plateau and La Burtignière (1000 m a.s.l.) in the Jura, Switzerland. Methods: Habitat distribution models based on high resolution remotely sensed data and vegetation field data are applied to monitor 130 mires. Instead of plant species or communities we used mean indicator values of vegetation records as response variables. To improve the differential power of indicator values for wetland habitat conditions, we calibrated these values using field data. Different methods were tested with our predictive models in three mires to see which calibration method is best in enhancing model performance. To assess the effect of the uneven distribution of vegetation records along environmental gradients, calibrations based on random and evenly distributed samples were compared. As a test of the predictive power of the models we used r² between ground truth and model prediction. This approach is illustrated through an application with nutrient indicator values in the mire La Burtignière. Results: Model performances were not the same for the three mires. The predictive power was better for the nutrient values, soil reaction and humus values than for light and moisture values. 2000 records were sufficient as basis for re‐calibration. Models based on original Landolt indicator values were overall the weakest compared with re‐calibrated values. By comparing the predictive power of Models based on randomly or evenly selected records were about equally predictive. Conclusions: 1. Ahabitat‐specific re‐calibration of the Landolt indicator values enhances the predictive mapping of the Swiss mire ecosystems. 2. The re‐calibration based on weighted averaging gives a better performance than the one based on Gaussian logistic regression. 3. The uneven distribution of indicator values due to the over‐representation of mire habitats does not hamper model performance. 4. 2000 vegetation records are a sufficient basis for an optimal re‐calibration of the vegetation types. An illustration of the method is given by using the soil fertility pattern of the mire La Burtignière.     

Determinants of distribution of six Pinus species in Catalonia,Spain

Abstract. This study explores the determinants of distribution, abundance and regeneration of six Pinus species (P. halepensis, P. nigra, P. pinaster, P. pinea, P. sylvestris and P. uncinata) that occur naturally in Catalonia, northeastern Spain. The aim of this study was to generate accurate predictions of the distribution of each species using simple and readily available environmental variables. We used recursive partitioning and GIS analyses to relate the data base of 10 600 field plots from the Forestry Inventory of Catalonia with abiotic and biotic characteristics of each plot. We present general patterns of distribution, dominance and regeneration for the six species and then focus on P. halepensis, the most abundant pine species in the western Mediterranean Basin. For all six species, the models correctly classified more than 80% of the distribution using abiotic factors, mainly altitude and rainfall variability. Biotic factors such as the basal area of other pine species were necessary to accurately predict patterns of pine species dominance. Biotic factors, especially the basal area of evergreen species (mainly Quercus ilex), were of overriding importance when predicting patterns of seedling occurrence. Potentially important factors such as land use and fire history were of little significance for predicting distribution at the scale of our study. Our models failed to predict accurately which species (and in which numbers) co‐occur with P. halepensis. Factors not included in this study, such as stand age, disturbance (cutting, clearing) and other human‐induced factors, are probably the main determinants of co‐existence patterns.     

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.     

Phenotypic distribution models corroborate species distribution models: A shift in the role and prevalence of a dominant prairie grass in response to climate change

Phenotypic distribution within species can vary widely across environmental gradients but forecasts of species’ responses to environmental change often assume species respond homogenously across their ranges. We compared predictions from species and phenotype distribution models under future climate scenarios for Andropogon gerardii, a widely distributed, dominant grass found throughout the central United States. Phenotype data on aboveground biomass, height, leaf width, and chlorophyll content were obtained from 33 populations spanning a ~1000 km gradient that encompassed the majority of the species’ environmental range. Species and phenotype distribution models were trained using current climate conditions and projected to future climate scenarios. We used permutation procedures to infer the most important variable for each model. The species‐level response to climate was most sensitive to maximum temperature of the hottest month, but phenotypic variables were most sensitive to mean annual precipitation. The phenotype distribution models predict that A. gerardii could be largely functionally eliminated from where this species currently dominates, with biomass and height declining by up to ~60% and leaf width by ~20%. By the 2070s, the core area of highest suitability for A. gerardii is projected to shift up to ~700 km northeastward. Further, short‐statured phenotypes found in the present‐day short grass prairies on the western periphery of the species’ range will become favored in the current core ~800 km eastward of their current location. Combined, species and phenotype models predict this currently dominant prairie grass will decline in prevalence and stature. Thus, sourcing plant material for grassland restoration and forage should consider changes in the phenotype that will be favored under future climate conditions. Phenotype distribution models account for the role of intraspecific variation in determining responses to anticipated climate change and thereby complement predictions from species distributions models in guiding climate adaptation strategies.     

Exploring species attributes and site characteristics to assess plant invasions in Spain

Aim Biological invasions are a major component of global change with increasing effects on natural ecosystems and human societies. Here, we aim to assess the relationship between plant invader species attributes and the extent of their distribution range size, at the same time that we assess the association between environmental factors and plant invader species richness. Location Spain, Mediterranean region. Methods From the species perspective, we calculated the distribution range size of the 106 vascular plant invaders listed in a recently published atlas of alien plant species in Spain. Range size was used as an estimation of the degree of invasion success of the species. To model variation in range size between species as a function of a set of species attributes, we adopted the framework of the generalized linear mixed models because they allow the incorporation of taxonomic categories as nested random factors to control for phylogenetic relationships. From the invaded site perspective, we determined invader plant species richness as the number of species for each 10 × 10 km Universal Transverse Mercator (UTM) grid. For each grid cell, we estimated variables concerning landscape, topography, climate and human settlement. Then, we performed a generalized linear mixed model incorporating a defined spatial correlation structure to assess the relationship between plant invader richness and the environmental predictors. Results From the species perspective, wind dispersal and minimum residence time appeared to favour invasion success. From the invaded site perspective, we identified high anthropogenic disturbance, low altitude, short distance to the coastline and dry, hot weather as the main correlates to UTM grid cell invader richness. Main conclusions According to these results, an increasing importance of man‐modified ecosystems and global warming in the Mediterranean region should facilitate the expansion of plant invaders, especially wind‐dispersed species, leading to the accumulation of invasive species in some sites (i.e. invasion hot spots).     

Testing the ability of species distribution models to infer variable importance

Models of species’ distributions and niches are frequently used to infer the importance of range- and niche-defining variables. However, the degree to which these models can reliably identify important variables and quantify their influence remains unknown. Here we use a series of simulations to explore how well models can 1) discriminate between variables with different influence and 2) calibrate the magnitude of influence relative to an ‘omniscient’ model. To quantify variable importance, we trained generalized additive models (GAMs), Maxent and boosted regression trees (BRTs) on simulated data and tested their sensitivity to permutations in each predictor. Importance was inferred by calculating the correlation between permuted and unpermuted predictions, and by comparing predictive accuracy of permuted and unpermuted predictions using AUC and the continuous Boyce index. In scenarios with one influential and one uninfluential variable, models failed to discriminate reliably between variables when training occurrences were < 8–64, prevalence was > 0.5, spatial extent was small, environmental data had coarse resolution and spatial autocorrelation was low, or when pairwise correlation between environmental variables was |r| > 0.7. When two variables influenced the distribution equally, importance was underestimated when species had narrow or intermediate niche breadth. Interactions between variables in how they shaped the niche did not affect inferences about their importance. When variables acted unequally, the effect of the stronger variable was overestimated. GAMs and Maxent discriminated between variables more reliably than BRTs, but no algorithm was consistently well-calibrated vis-à-vis the omniscient model. Algorithm-specific measures of importance like Maxent's change-in-gain metric were less robust than the permutation test. Overall, high predictive accuracy did not connote robust inferential capacity. As a result, requirements for reliably measuring variable importance are likely more stringent than for creating models with high predictive accuracy.     

Putting competition strategies into ideal free distribution models: habitat selection as a tug of war

When resources are patchily distributed in an environment, behavioral ecologists frequently turn to ideal free distribution (IFD) models to predict the spatial distribution of organisms. In these models, predictions about distributions depend upon two key factors: the quality of habitat patches and the nature of competition between consumers. Surprisingly, however, no IFD models have explored the possibility that consumers modulate their competitive efforts in an evolutionarily stable manner. Instead, previous models assume that resource acquisition ability and competition are fixed within species or within phenotypes. We explored the consequences of adaptive modulation of competitive effort by incorporating tug-of-war theory into payoff equations from the two main classes of IFD models (continuous input (CI) and interference). In the models we develop, individuals can increase their share of the resources available in a patch, but do so at the costs of increased resource expenditures and increased negative interactions with conspecifics. We show how such models can provide new hypotheses to explain what are thought to be deviations from IFDs (e.g., the frequent observation of fewer animals than predicted in "good" patches of habitat). We also detail straightforward predictions made uniquely by the models we develop, and we outline experimental tests that will distinguish among alternatives.     

Evaluating the ecological realism of plant species distribution models with ecological indicator values

Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modeled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modeled niche optima obtained from European-scale SDMs of 1476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modeled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modeled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques.     

Occupancy models including local and landscape variables are useful to assess the distribution of a salamander species at risk

Numerous amphibian species are at risk of extinction worldwide. Therefore, reliable estimations of the distribution and abundance of these species are necessary for their conservation. Generally, amphibians are difficult to detect in the wild, which compromises the accuracy of long-term population monitoring and management. Occupancy models are useful tools to assess how environmental variables, at a local and at a landscape scale, affect the distribution and abundance of organisms taking into account species imperfect detectability. In this study, we evaluated with an environmental multiscale approach the seasonal variation of the occupation area of the threatened salamander, Ambystoma ordinarium along its distribution range. We obtained readings in 60 streams of physicochemical variables associated with habitat quality and landscape features. We found that detection and occupation probability of A. ordinarium are seasonally associated with different environmental variables. During the dry season, detectability was positively associated with temperature and stream depth, whereas occupancy was positively associated with the proportion of crops in the landscape and stream elevation. In the rainy season, the detection probability was not explained by any variable considered, and occupancy was negatively associated with stream's electrical conductivity and dissolved oxygen. Based on the estimation of occupied sites, we showed that A. ordinarium presents a more restricted distribution range than previously projected. Therefore, our results reveal the importance of evaluating the accuracy of distribution estimates for the conservation of threatened species as A. ordinarium.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Leaf morphology and leaf chemical composition in three Quercus (Fagaceae) species along a rainfall gradient in NE Spain

 Leaf features were examined in three Quercus species (Q. coccifera, Q. ilex and Q. faginea) along a steep rainfall gradient in NE Spain. The analyzed leaf traits were area, thickness, density, specific mass, leaf concentration of nitrogen, phosphorous, lignin, cellulose and hemicellulose, both on a dry weight basis (N_w, P_w, L_w, C_w, H_w) and on an area basis (N_a, P_a, L_a, C_a, H_a). These traits were regressed against annual precipitation and correlated with each other, revealing different response patterns in the three species. Q. faginea, a deciduous tree, did not show any significant correlation with rainfall. In Q. coccifera, an evergreen shrub, N_w, N_a, L_w, L_a and C_a increased with higher annual rainfall, while H_w decreased. In Q. ilex, an evergreen tree, leaf area, P_w and L_w increased with precipitation, whereas specific leaf mass, thickness and H_a showed the reverse response. Correlations between the leaf features revealed that specific mass variation in Q. faginea and Q. coccifera could be explained by changes in leaf density, while in Q. ilex specific leaf mass was correlated with thickness. Specific leaf mass in the three species appeared positively correlated with all the chemical components on a leaf area basis except with lignin in Q. ilex and with P in Q. ilex and Q. faginea. In these two tree species P_w showed a negative correlation with specific leaf mass. It is suggested that each species has a different mechanism to cope with water shortage which is to a great extent related to its structure as a whole, and to its habit. Received: 18 December 1995 / Accepted: 8 March 1996     

Global asymptotic stability of the size distribution in probabilistic models of the cell cycle

Probabilistic models of the cell cycle maintain that cell generation time is a random variable given by some distribution function, and that the probability of cell division per unit time is a function only of cell age (and not, for instance, of cell size). Given the probability density, f(t), for time spent in the random compartment of the cell cycle, we derive a recursion relation for _n(x), the probability density for cell size at birth in a sample of cells in generation n. For the case of exponential growth of cells, the recursion relation has no steady-state solution. For the case of linear cell growth, we show that there exists a unique, globally asymptotically stable, steady-state birth size distribution, _*(x). For the special case of the transition probability model, we display _*(x) explicitly.This work was supported by the National Science Foundation under grants MCS8301104 (to J.J.T.) and MCS8300559 (to K.B.H.), and by the National Institutes of Health under grant GM27629 (to J.J.T.).     

Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps

Aim To test the effectiveness of statistical models based on explanatory environmental variables vs. existing distribution information (maps and breeding atlas), for predicting the distribution of four species of raptors (family Accipitridae): common buzzard Buteo buteo (Linnaeus, 1758), short‐toed eagle Circaetus gallicus (Gmelin, 1788), booted eagle Hieraaetus pennatus (Gmelin, 1788) and black kite Milvus migrans (Boddaert, 1783). Location Andalusia, southern Spain. Methods Generalized linear models of 10 × 10 km squares surveyed for the presence/absence of the species by road census. Statistical models use as predictors variables derived from topography, vegetation and land‐use, and the geographical coordinates (to take account of possible spatial trends). Predictions from the models are compared with current distribution maps from the national breeding atlas and leading reference works. Results The maps derived from statistical models for all four species were more predictive than the previously published range maps and the recent national breeding atlas. The best models incorporated both topographic and vegetation and land‐use variables. Further, in three of the four species the inclusion of spatial coordinates to account for neighbourhood effects improved these models. Models for the common buzzard and black kite were highly predictive and easy to interpret from an ecological point of view, while models for short‐toed eagle and, particularly, booted eagle were not so easy to interpret, but still predicted better than previous distribution information. Main conclusions It is possible to build accurate predictive models for raptor distribution with a limited field survey using as predictors environmental variables derived from digital maps. These models integrated in a geographical information system produced distribution maps that were more accurate than previously published ones for the study species in the study area. Our study is an example of a methodology that could be used for many taxa and areas to improve unreliable distribution information.     

A phenological hypothesis on the thermophilous distribution of Pistacia lentiscus L.

In this study we present evidences of the importance of the phenological pattern on the distribution limits of the dioecious Pistacia lentiscus L. This species, though displaying a thermophilous distribution, has been proved to resist low freezing temperatures during winter. We try to explain this apparent paradox by studying the effects of an extreme cold event that occurred in December 2001 on a natural population of P. lentiscus in the NE of the Iberian Peninsula. In previous phenological studies conduced over 4 years, no spring branch damage was noticeable among population individuals. Female individuals of this species present a prolonged phenophase development which extends until early winter with the conclusion of fruit set. Therefore, we hypothesize that female plants would be more affected than male ones in terms of the following: (1) vegetative and reproductive organs survival; and (2) next-year vegetative growth and fruit production. To test this hypothesis, we selected 225 adult individuals (113 females and 112 males) in April 2002, and estimated their crown volume, percentage of frozen branches and reproductive buds, and the amount of 2001 fruits frozen. In June 2002 we evaluated, in the same individuals, the percentage of vegetative buds flushed into shoots and of reproductive buds producing infrutescences. Branch mortality was significantly higher in female plants and females with increased frost-damage displayed a higher amount of frozen fruits. The loss of reproductive buds caused a decrease in 2002 fruit production, while 2002 vegetative growth was unaffected by the degree of frost damage. These results verify most of the predictions of our hypothesis. Moreover, they suggest that the limited distribution of P. lentiscus in the cold areas of Mediterranean climate could be more determined by the long extent of the phenological activity of the crown than by its frost tolerance during winter.     

物种分布模型理论研究进展

利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此,总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。