High-resolution bioclimatic dataset derived from future climate projections for plant species distribution modeling

Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets.     

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change--particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km(2) study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.     

Formation of disjunct plant distributions in Northeast Asia: a case study of <Emphasis Type="Italic">Betula davurica</Emphasis> using a species distribution model

Repeated climate change during glacial and interglacial periods of the Quaternary led to mass migrations that resulted in disjunct distributions for many species. However, few studies have examined the processes that form disjunct distributions in Northeast Asia (NEA). In this study, we examined the disjunct distribution of Betula davurica Pall. in the Japanese archipelago. This species is a dominant canopy tree found in cool-temperate deciduous broad-leaved forests of continental NEA. We hypothesized that Quaternary climate change caused the present disjunct distribution pattern of this species. To test this hypothesis, we adopted a species distribution model and examined a series of potential habitats in the Last Glacial Maximum (LGM), the mid-Holocene, and the present. We generated models in MaxEnt with B. davurica presence as the response variable and six bioclimatic variables as predictor variables. During the LGM, projected potential habitats were distributed around the Korean Peninsula, East China, and the Japanese archipelago, excluding Hokkaido. In the mid-Holocene, habitats retreated both from East China and western Japan, remained unchanged in the Korean Peninsula and central Honshu mountains, and expanded to northern China, the Russian Far East, as well as northern Japan (Hokkaido). Thus, post-LGM global warming led to an expansion of B. davurica distribution to northern parts of continental NEA, along with a retreat in the Japanese archipelago. This shift in populations formed the present disjunct distribution.     

Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks

Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub‐disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species’ presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change.     

How to survive a glaciation: the challenge of estimating biologically realistic potential distributions under freezing conditions

Correlative ecological niche models are increasingly used to estimate potential distributions during the Last Glacial Maximum (LGM) for biogeographical research. In the case of presence‐background/pseudoabsences techniques, cold environments that are poorly represented in existing geography can complicate the process of model calibration and transfer into more extreme cold environments that were very common during the LGM (non‐analog conditions). This may lead to biologically unrealistic estimations. Using one cold‐adapted North American mammal, we explore a real scenario to better understand the effect of restricting the range of environmental conditions over which niche models are calibrated and then transferred to LGM conditions. We performed two sets of experiments in Maxent: 1) we calibrated models in the context of only present‐day climate conditions, which is the most common practice, and compared predictions under LGM conditions based on two extrapolation methods (clamping versus unconstrained); 2) we calibrated single models using both present‐day and LGM conditions as part of the same background in order to include more extreme environments in the model calibration. Our experiments led to dramatically different estimates of species’ potential distributions, showing notable differences with respect to latitudinal and elevational shifts during the LGM. Models calibrated using present‐day climates yielded biologically unrealistic estimations, suggesting that species survived in the glaciers during the LGM. Even more unrealistic estimations were achieved when clamping was enforced as the method to extrapolate. Models calibrated in the context of both modern and past climates reduced the required degree of extrapolation and allowed more realistic potential distributions, suggesting that the species avoided extremely cold conditions during the LGM. This study alerts to the possibility of obtaining implausible potential distributions during the LGM due to restricted background datasets and offers recommendations that should promote better strategies to estimate distributional changes during glaciations.     

Toward ecologically realistic predictions of species distributions: A cross‐time example from tropical montane cloud forests

There is an urgent need for more ecologically realistic models for better predicting the effects of climate change on species’ potential geographic distributions. Here we build ecological niche models using MAXENT and test whether selecting predictor variables based on biological knowledge and selecting ecologically realistic response curves can improve cross‐time distributional predictions. We also evaluate how the method chosen for extrapolation into nonanalog conditions affects the prediction. We do so by estimating the potential distribution of a montane shrew (Mammalia, Soricidae, Cryptotis mexicanus) at present and the Last Glacial Maximum (LGM). Because it is tightly associated with cloud forests (with climatically determined upper and lower limits) whose distributional shifts are well characterized, this species provides clear expectations of plausible vs. implausible results. Response curves for the MAXENT model made using variables selected via biological justification were ecologically more realistic compared with those of the model made using many potential predictors. This strategy also led to much more plausible geographic predictions for upper and lower elevational limits of the species both for the present and during the LGM. By inspecting the modeled response curves, we also determined the most appropriate way to extrapolate into nonanalog environments, a previously overlooked factor in studies involving model transfer. This study provides intuitive context for recommendations that should promote more realistic ecological niche models for transfer across space and time.     

Niche and area of distribution modeling: a population ecology perspective

Statistical modeling of areas of distribution of species by correlative analysis of the environmental features of known presences has become widespread. However, to a large degree, the logic and the functioning of many of these applications remain obscure, not only due to the fact that some of the modeling methods are intrinsically complex (neural networks, genetic algorithms, generalized additive models, for example), but mainly because the role of other ecological processes affecting the species distributions sometimes is not explicitly stated. Resorting to fundamental principles of population ecology, a scheme of analysis based on separation of three factors affecting species distributions (environment, biotic interactions and movements) is used to clarify some results of niche modeling exercises. The area of distribution of a virtual species which was generated by both environmental and biotic factors serves to illustrate the possibility that, at coarse resolutions, the distribution can be approximately recovered using only information about the environmental factors and ignoring the biotic interactions. Finally, information on the distribution of a butterfly species, Baronia brevicornis , is used to illustrate the importance of interpreting the results of niche models by including hypothesis about one class of movements. The results clarify the roles of the three factors in interpreting the results of using correlative approaches to modeling species distributions or their niches.     

Improving the use of environmental diversity as a surrogate for species representation

The continuous p‐median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach to ED is computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p‐median for datasets of South African animals. We tested whether maxdisp performs as well as continuous p‐median for 12 datasets that included plants and other continents, and whether particular types of environmental variables produced consistently better models of ED. We selected 12 species inventories and atlases to span a broad range of taxa (plants, birds, mammals, reptiles, and amphibians), spatial extents, and resolutions. For each dataset, we used continuous p‐median ED and maxdisp ED in combination with five sets of environmental variables (five combinations of temperature, precipitation, insolation, NDVI, and topographic variables) to select environmentally diverse sites. We used the species accumulation index (SAI) to evaluate the efficiency of ED in representing species for each approach and set of environmental variables. Maxdisp ED represented species better than continuous p‐median ED in five of 12 biodiversity datasets, and about the same for the other seven biodiversity datasets. Efficiency of ED also varied with type of variables used to define environmental space, but no particular combination of variables consistently performed best. We conclude that maxdisp ED performs at least as well as continuous p‐median ED, and has the advantage of faster and simpler computation. Surprisingly, using all 38 environmental variables was not consistently better than using subsets of variables, nor did any subset emerge as consistently best or worst; further work is needed to identify the best variables to define environmental space. Results can help ecologists and conservationists select sites for species representation and assist in conservation planning.     

Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species’ range dynamics over geologic time

Understanding the factors that shape species’ distributions is a key topic in biogeography. As climates change, species can either cope with these changes through evolution, plasticity or by shifting their ranges to track the optimal climatic conditions. Ecological niche modeling (ENM) is a widespread technique in biogeography that estimates the niche of the organism by using occurrences and environmental data to estimate species’ potential distributions. ENMs are often criticized for failing to take species’ dispersal abilities into consideration. Here, we attempt to fill this gap by combining ENMs with dispersal and corridor modeling to study the range dynamics of North American spadefoot toads (Scaphiopodidae) over the Holocene. We first estimated the current and past distributions of spadefoot toads and then estimated their past distributions from the Last Glacial Maximum (LGM) to the present day. Then, we estimated how each taxon recolonized North American by using dispersal and corridor modeling. By combining these two modeling approaches we were able to 1) estimate the LGM refugia used by the North American spadefoot toads, 2) further refine these projections by estimating which of the putative LGM refugia contributed to the recolonization of North America via dispersal, and 3) estimate the relative influence of each LGM refugium to the current species’ distributions. The models were tested using previously published phylogeographic data, revealing a high degree of congruence between our models and the genetic data. These results suggest that combining ENMs and dispersal modeling over time is a promising approach to investigate both historical and future species’ range dynamics.     

Palaeodistribution modelling does not support disjunct Pleistocene refugia in several Central American plant taxa

Aim We combine evidence from palaeoniche modelling studies of several tree species to estimate the extent of Central American forest during the Last Glacial Maximum (LGM). In particular, we ask whether the distributions of these species are likely to have changed since the LGM, and whether LGM distributions coincide with previously proposed Pleistocene refugia in this area. Location Central American wet and seasonally dry forests. Methods We developed ecological niche models using two simulations of Pleistocene climate and occurrence data for 15 Neotropical plant species. We focused on palaeodistribution models of three ‘focal’ tree species that occur in wet and seasonally dry Central American forests, where recent phylogeographic data suggest Pleistocene differentiation coincident with previously proposed refugia. We added predictions from six wet‐forest and six seasonally dry‐forest obligate plant species to gauge whether Pleistocene range shifts were specific to habitat type. Correlation analyses were performed between projected LGM and present distributions, LGM distributions and previously proposed refugia. We also asked whether modelled palaeodistributions were smaller than their current extents. Results According to our models, the ranges of the study species were not reduced during the LGM, and did not correlate with refugial models, regardless of habitat type. Relative range sizes between present and LGM distributions did not indicate significant range changes since the LGM. However, relative range sizes differed overall between the two palaeoclimate models. Main conclusions Many of the modelled palaeodistributions of study species were not restricted to refugia during the LGM, regardless of forest type. While constrained from higher elevations, most species found suitable habitat at coastal margins and on newly exposed land due to lowered sea levels during the LGM. These results offer no corroboration for Pleistocene climate change as a driver of genetic differentiation in the ‘focal’ species. We offer alternative explanations for genetic differentiation found in plant species in this area.     

Geographical distribution modelling of the bronze bug: a worldwide invasion

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Effects of Quaternary climatic fluctuations on the distribution of Neotropical savanna tree species

In order to develop niche models for tree species characteristic of the cerrado vegetation (woody savannas) of central South America, and to hindcast their distributions during the Last Glacial Maximum and Last Inter‐Glacial, we compiled a dataset of tree species checklists for typical cerrado vegetation (n = 282) and other geographically co‐occurring vegetation types, e.g. seasonally dry tropical forest (n = 355). We then performed an indicator species analysis to select ten species that best characterize typical cerrado vegetation and developed niche models for them using the Maxent algorithm. We used these models to assess the probability of occurrence of each species across South America at the following time slices: Current (0 ka pre‐industrial), Holocene (6 ka BP), Last Glacial Maximum (LGM – 21 ka BP), and Last Interglacial (LIG – 130 ka BP). The niche models were robust for all species and showed the highest probability of occurrence in the core area of the Cerrado Domain. The palaeomodels suggested changes in the distributions of cerrado tree species throughout the Quaternary, with expansion during the LIG into the adjacent Amazonian and Atlantic moist forests, as well as connections with other South American savannas. The LGM models suggested a retraction of cerrado vegetation to inter‐tableland depressions and slopes of the Central Brazilian Highlands. Contrary to previous hypotheses, such as the Pleistocene refuge theory, we found that the widest expansion of cerrado tree species seems to have occurred during the LIG, most probably due to its warmer climate. On the other hand, the postulated retractions during the LGM were likely related to both decreased precipitation and temperature. These results are congruent with palynological and phylogeographic studies in the Cerrado Domain.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time‐consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence‐only data. We evaluate our method on independently collected high‐quality presence‐absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce “hinge features” that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore “background sampling” strategies that cope with sample selection bias and decrease model‐building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence‐only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) “target‐group” background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.     

Multispectral canopy reflectance improves spatial distribution models of Amazonian understory species

Species distribution models are required for the research and management of biodiversity in the hyperdiverse tropical forests, but reliable and ecologically relevant digital environmental data layers are not always available. We here assess the usefulness of multispectral canopy reflectance (Landsat) relative to climate data in modelling understory plant species distributions in tropical rainforests. We used a large dataset of quantitative fern and lycophyte species inventories across lowland Amazonia as the basis for species distribution modelling (SDM). As predictors, we used CHELSA climatic variables and canopy reflectance values from a recent basin-wide composite of Landsat TM/ETM+ images both separately and in combination. We also investigated how species accumulate over sites when environmental distances were expressed in terms of climatic or surface reflectance variables. When species accumulation curves were constructed such that differences in Landsat reflectance among the selected plots were maximised, species accumulated faster than when climatic differences were maximised or plots were selected in a random order. Sixty-nine species were sufficiently frequent for species distribution modelling. For most of them, adequate SDMs were obtained whether the models were based on CHELSA data only, Landsat data only or both combined. Model performance was not influenced by species’ prevalence or abundance. Adding Landsat-based environmental data layers overall improved the discriminatory capacity of SDMs compared to climate-only models, especially for soil specialist species. Our results show that canopy surface reflectance obtained by multispectral sensors can provide studies of tropical ecology, as exemplified by SDMs, much higher thematic (taxonomic) detail than is generally assumed. Furthermore, multispectral datasets complement the traditionally used climatic layers in analyses requiring information on environmental site conditions. We demonstrate the utility of freely available, global remote sensing data for biogeographical studies that can aid conservation planning and biodiversity management.     

The effect of sample size and species characteristics on performance of different species distribution modeling methods

Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.     

Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients

A fundamental goal of ecology is to understand the determinants of species' distributions (i.e., the set of locations where a species is present). Competition among species (i.e., interactions among species that harms each of the species involved) is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large‐scale effects of competition or other biotic interactions is to fit species' distributions models (SDMs) and assess the effect of competitors on the distribution and abundance of the species of interest. It is often difficult to validate the accuracy of this approach with available data. Here, we simulate virtual species that experience competition. In these simulated datasets, we can unambiguously identify the effects that competition has on a species' distribution. We then fit SDMs to the simulated datasets and test whether we can use the outputs of the SDMs to infer the true effect of competition in each simulated dataset. In our simulations, the abiotic environment influenced the effects of competition. Thus, our SDMs often inferred that the abiotic environment was a strong predictor of species abundance, even when the species' distribution was strongly affected by competition. The severity of this problem depended on whether the competitor excluded the focal species from highly suitable sites or marginally suitable sites. Our results highlight how correlations between biotic interactions and the abiotic environment make it difficult to infer the effects of competition using SDMs.     

Ice age distributions of European small mammals: insights from species distribution modelling

Aim In addition to the traditionally recognized Last Glacial Maximum (LGM, 21 ka) refuge areas in the Mediterranean region, more northerly LGM distributions for temperate and boreal taxa in central and eastern Europe are increasingly being discussed based on palaeoecological and phylogeographical evidence. Our aim was to investigate the potential refuge locations using species distribution modelling to estimate the geographical distribution of suitable climatic conditions for selected rodent species during the LGM. Location Eurasia. Methods Presence/absence data for seven rodent species with range limits corresponding to the limits of temperate or boreal forest or arctic tundra were used in the analysis. We developed predictive distribution models based on the species present‐day European distributions and validated these against their present‐day Siberian ranges. The models with the best predictors of the species distributions across Siberia were projected onto LGM climate simulations to assess the distribution of climatically suitable areas. Results The best distribution models provided good predictions of the present‐day Siberian ranges of the study species. Their LGM projections showed that areas with a suitable LGM climate for the three temperate species (Apodemus flavicollis, Apodemus sylvaticus and Microtus arvalis) were largely restricted to the traditionally recognized southern refuge areas, i.e. mainly in the Mediterranean region, but also southernmost France and southern parts of the Russian Plain. In contrast, suitable climatic conditions for the two boreal species (Clethrionomys glareous and Microtus agrestis) were predicted as far north as southern England and across southern parts of central and eastern Europe eastwards into the Russian Plain. For the two arctic species (Lemmus lemmus and Microtus oeconomus), suitable climate was predicted from the Atlantic coast eastward across central Europe and into Russia. Main conclusions Our results support the idea of more northerly refuge areas in Europe, indicating that boreal species would have found suitable living conditions over much of southern central and eastern Europe and the Russian Plain. Temperate species would have primarily found suitable conditions in the traditional southern refuge areas, but interestingly also in much of the southern Russian Plain.     

利用分布有/无数据预测物种空间分布的研究方法综述

详细的物种地理分布信息是生态学研究和制定保护策略的基础。相比较于直接估测种群数量,获取物种分布的有/无数据更为实用。因此,利用分布有/无数据并结合环境变量建立模型预测物种空间分布的方法在近年来得到了长足发展,并被广泛应用。利用分布有/无数据预测物种分布,关键的步骤包括：1)构建总体概念模型,2)收集物种分布有/无数据,并准备环境变量图层;3)选择合适的统计模型和算法,以及4)对模型进行评估。概念模型提出研究假设,并确定数据收集及模型方法。收集物种分布数据有系统调查及非系统调查方法。筛选并准备与物种分布相关的环境变量,利用GIS工具处理,使之成为符合模型条件的具有合适的空间尺度的数字化图层。利用环境变量和物种分布有/无的数据,选择合适的方法及软件建立模型,并对模型进行检验和评估。我们总结了用于构建物种分布模型的不同算法和软件。本文将针对以上各个环节,阐述利用物种分布有/无数据进行研究所需要的技术细节,以期望为读者提供借鉴。     

Maxent modeling for predicting the potential distribution of Sanghuang,an important group of medicinal fungi in China

The ability to identify the spatial distribution of economically important fungal species is crucial for understanding the environmental factors that affect them and for conservation management. A potentially valuable approach for this is maximum entropy (Maxent) spatial distribution modeling, which was applied here to map the potential distribution of three “Sanghuang” mushrooms in China, which include Phellinus baumii, Phellinus igniarius and Phellinus vaninii. Nineteen WorldClim bioclimatic variables, with corresponding altitude data, and 89 spatially well-dispersed species occurrence records were used in the modeling. The relative importance of the environmental variables was evaluated by Jackknife tests in the modeling analysis. The maximum entropy models obtained have high Area Under Receiver Operating Characteristic Curve (AUC) values: 0.956, 0.967 and 0.960, for P. baumii, P. igniarius and P. vaninii, respectively. The bioclimatic variable that most strongly affected distributions of P. baumii and P. vaninii was precipitation in the warmest quarter, while the mean temperature in the warmest quarter affected the distribution of P. igniarius most strongly. Overall, these models could provide valuable help in searching for the target species in areas where it is hitherto unknown, and be the reference of conservation measures for these medicinal fungal species.