Regional spatial modeling of topsoil geochemistry

Summary .  Geographic information about the levels of toxics in environmental media is commonly used in regional environmental health studies when direct measurements of personal exposure is limited or unavailable. In this article, we propose a statistical framework for analyzing the spatial distribution of topsoil geochemical properties, including the concentrations of various toxicants. Due to the small-scale heterogeneity of most geochemical topsoil processes, direct measurements of the processes themselves only provide highly localized information; it is thus financially prohibitive to study the spatial patterns of these processes across a large region using traditional geostatistical analyses of point-referenced topsoil data. Instead, it is standard practice to assess geochemical patterns at a regional scale using point-referenced measurements collected in stream sediment because, unlike topsoil data, individual stream sediment geochemical measurements are representative of the surrounding area. We propose a novel multiscale soils (MSS) model that formally synthesizes data collected in topsoil and stream sediment and allows the richer stream sediment information to inform about the topsoil process, which in environmental health studies is typically more relevant. Our model accommodates the small-scale heterogeneity of topsoil geochemical processes by modeling spatial dependence at an aggregate resolution corresponding to hydrologically similar regions known as watersheds. We present an analysis of the levels of arsenic, a toxic heavy metal, in topsoil across the midwestern United States using the MSS model and show that this model has better predictive abilities than alternative approaches using more conventional statistical models for point-referenced spatial data.     

排序和广义线性模型与广义可加模型在植物种与环境关系研究中的应用

排序和广义线性模型(Generalized Linear Model,GLM)与广义可加模型(Goneralized Additive Model,GAM)是研究植物种与环境间关系的重要方法。基于线性模型的排序方法应限定于环境梯度较短的植被数据。而基于单峰模型的排序方法更适用于梯度较长的情况。PCA、CA／RA系列和CCA系列是常用的排序方法。同时进行环境数据和植被数据分析的CCA系列,能清楚地得出植物种与环境间的关系。CCA改进后的DCCA和PCCA,是现今较理想的排序方法。GLM和GAM实质上是用环境变量的高阶多项式来拟合植物种与环境变量的关系。GLM和GAM扩展了植物种与环境变量之间的关系模型,能深入地探讨植物种与环境间的关系。GLM主要是模型决定的,而GAM主要取决于原始数据。一般来说,排序能得出研究区域的主要环境梯度,提供了物种聚集和植物群落的概略描述。GLM与GAM对于深入研究单个植物种与环境间的关系具有优势。在实际研究中,两种方法结合使用能互补不足。     

Habitat distribution models,spatial autocorrelation,functional traits and dispersal capacity of alpine plant species

Abstract. We evaluate the potential influence of disturbance on the predictability of alpine plant species distribution from equilibrium‐based habitat distribution models. Firstly, abundance data of 71 plant species were correlated with a comprehensive set of environmental variables using ordinal regression models. Subsequently, the residual spatial autocorrelation (at distances of 40 to 320 m) in these models was explored. The additional amount of variance explained by spatial structuring was compared with a set of functional traits assumed to confer advantages in disturbed or undisturbed habitats. We found significant residual spatial autocorrelation in the habitat models of most of the species that were analysed. The amount of this autocorrelation was positively correlated with the dispersal capacity of the species, levelling off with increasing spatial scale. Both trends indicate that dispersal and colonization processes, whose frequency is enhanced by disturbance, influence the distribution of many alpine plant species. Since habitat distribution models commonly ignore such spatial processes they miss an important driver of local‐ to landscape‐scale plant distribution.     

白马鸡繁殖早期栖息地选择和空间分布

2003年1-6月和2004年4-6月考察了分布于四川省稻城县著杰寺周围的白马鸡(Crossoptiloncrossop-tilon)种群,分析繁殖早期白马鸡繁殖对的栖息地选择和空间分布特征。雌雄个体配对前,对整个研究区域进行系统取样并测量若干环境变量的参数。配对后,利用随机样线调查繁殖对出现的位置和数量。有繁殖对出现的栅格定义为探测栅格,反之为非探测栅格。水源距离、灌木盖度、灌木高度和草本高度等变量在探测和非探测栅格间差异显著。将显著差异变量及这些变量间的一级互作经单变量逻辑斯蒂回归进行筛选,以保留变量为自变量进行向前筛选的逐步逻辑斯蒂回归,最后选择具有最小AICC值的回归等式为最佳的回归模型。模型表明繁殖对的栖息地选择与水源距离负相关,与灌木盖度正相关。2004年进行了重复调查,获得实际的探测和非探测栅格与模型预测值之间无显著差异。计算繁殖对在0°(东-西),45°(东北-西南),90°(南-北)和135°(西北-东南)4个方向上的变异函数。结果表明:繁殖对的方向性变异函数可用球状模型来拟合,拟合曲线和实验曲线间的拟合程度达到显著水平,繁殖对呈明显的各向异性聚集分布[动物学报51(3):383-392,2005]。     

Evaluation of the spatial distribution of Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae) in South Korea combining climate and host plant distribution

Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae), a global forest pest, has a potential to damage forests in South Korea, requiring an effective tool for evaluating its potential distribution. This study aimed to evaluate the spatial distribution of A. glabripennis in South Korea by simultaneously considering climate and host plants. Climatic suitability was firstly evaluated using a CLIMEX model; then, it was combined with the areal distribution of host plants using a simple mathematical formulation. We finally projected the spatial distribution of A. glabripennis onto the map of administrative districts to identify hazardous areas to watch. As a result, the developed model predicted that over 40% of areas in South Korea could be exposed to A. glabripennis damage, and most of them were located in mountainous areas with abundant host plants. In addition, climatic suitability was higher in coastal areas, which was different than a previous record of A. glabripennis occurrence, while the prediction by a comprehensive model was consistent with the record. In conclusion, the model including both climate and host plant occurrence was more reliable than the model which only included climate, and could provide useful data for determining areas for monitoring and control.     

Effects of grazing on plant species composition and spatial distribution in rangelands of the Sahel

The effects of grazing by livestock on plant species composition and spatial distribution have been studied at Sadoré, Niger. Herbaceous species were recorded in plots of increasing size from 1/64 to 1024 m² in ten fallow plots subjected to five different grazing treatments over the previous three years. Treatments consisted of three intensities of grazing, and of protection from grazing for either 3 or 14 years. For all treatments, the number of species fitted a normal distribution with the log (ln) of the area inventoried. However, the fit improved slightly when the model included two successive log-normal distributions respectively considered as species distribution within a patch and at the patch mosaic scale. Across treatments, the optimal sampling areas averaged 3.8 ± 1.1 m² for the within-patch and 725 ± 113 m² for the mosaic scale distributions. It is argued that similarity between treatments in the overall log-normal distribution resulted from compensations between the divergent trends that affected species distribution within and across patches depending on the grazing status. Long-term protection resulted in a regular spatial arrangement of highly contrasted, but internally homogeneous patches. Heavy grazing ensued the dominance of a few species in contagious patches but also left niches for scattered individuals of other species. Tests of the relative frequency of each plant species, together with the average area needed to record that species, were used to characterize specific response to grazing. A majority of species encountered in old Sahelian fallows were either fostered by grazing, indifferent or tolerant to grazing. However, more than a third of the species appeared sensitive to heavy grazing, and no relationships were found between species response to grazing and palatability.     

Factors influencing the spatial distribution of forest plant species in hedgerows of North-western Germany

In North-western Germany woodland fragmentation has caused a decline in many forest plant species. Hedgerows partly offer a similar environment as forests and have been identified as potential habitats for forest plants in various studies from North America and Western Europe. The objective of this study was to examine whether this applies also to Central Europe and which variables affect the spatial distribution and abundance of forest plant species in hedgerows on a local scale. Three hedgerow networks north of the city of Bremen, Germany, were selected as study areas and divided into totally 515 hedgerow segments. In each segment we recorded all vascular plants and a large number of explanatory variables relating to structure, spatial configuration, environment and management. Averaged across species there was a predominant effect of environmental factors on the occurrence of forest species in the hedgerows, followed by spatial configuration and management. Hedgerow structure was found to be less important. In general, forest species were favored by low nutrient and light availability as well as high connectivity with other hedgerows or forest; they avoided hedgerows with a west-easterly orientation and an adjacent land use in the form of fields or grasslands. Forest species found and not found in hedgerows did not differ in their environmental preferences or life history traits. The number of threatened forest species in the hedgerows, however, was lower than expected with respect to their overall proportion to the total number of forest species in the region.     

Determining the factors affecting the distribution of Muscari latifolium,an endemic plant of Turkey,and a mapping species distribution model

Species distribution modeling was used to determine factors among the large predictor candidate data set that affect the distribution of Muscari latifolium , an endemic bulbous plant species of Turkey, to quantify the relative importance of each factor and make a potential spatial distribution map of M. latifolium . Models were built using the Boosted Regression Trees method based on 35 presence and 70 absence records obtained through field sampling in the Gönen Dam watershed area of the Kazda?? Mountains in West Anatolia. Large candidate variables of monthly and seasonal climate, fine‐scale land surface, and geologic and biotic variables were simplified using a BRT simplifying procedure. Analyses performed on these resources, direct and indirect variables showed that there were 14 main factors that influence the species’ distribution. Five of the 14 most important variables influencing the distribution of the species are bedrock type, Quercus cerris density, precipitation during the wettest month, Pinus nigra density, and northness. These variables account for approximately 60% of the relative importance for determining the distribution of the species. Prediction performance was assessed by 10 random subsample data sets and gave a maximum the area under a receiver operating characteristic curve (AUC) value of 0.93 and an average AUC value of 0.8. This study provides a significant contribution to the knowledge of the habitat requirements and ecological characteristics of this species. The distribution of this species is explained by a combination of biotic and abiotic factors. Hence, using biotic interaction and fine‐scale land surface variables in species distribution models improved the accuracy and precision of the model. The knowledge of the relationships between distribution patterns and environmental factors and biotic interaction of M. latifolium can help develop a management and conservation strategy for this species.     

Predictive mapping of alpine grasslands in Switzerland: Species versus community approach

Abstract. Separate logistic regression models were developed to predict the distribution and large-scale spatial patterns of dominant graminoid species and communities in alpine grasslands. The models are driven by four bioclimatic parameters: degree-days of growing season (basis 0 °C), a moisture index for July, potential direct solar radiation for March, and a continentality index. Geology and slope angle were used as a surrogate for nutrient availability and soil water capacity. The bioclimatic parameters were derived from monthly mean temperature, precipitation, cloudiness and potential direct solar radiation. The environmental parameters were interpolated using a digital elevation model with a resolution of 50 m. The vegetation data for model calibration originate from field surveys and literature. An independent test data set with samples from three different climatic zones was used to test the model. The degree of coincidence between simulated and observed patterns was similar for species and communities, but the κ-values for communities were generally higher (κ= 0.539) than for species (mean individual κ= 0.201). Information on land use was detected as a major factor that could significantly improve both the species and the community model. Nevertheless, the climatic factors used to drive the model explained a major part of the observed patterns.     

Subcellular distribution, multiple forms and development of glutamate-pyruvate (glyoxylate) aminotransferase in plant tissues

According to a sucrose density gradient analysis of cell organelles from homogenates of green leaves of rye, wheat and pea seedlings glutamate-pyruvate aminotransferase was predominantly localized in the leaf microbodies (peroxisomes; 90%) and to a minor extent in the mitochondria (10%) but completely absent from chloroplasts. In etiolated rye leaves the distribution of the enzyme was similar. In other non-green tissues glutamate-pyruvate aminotransferase was predominantly associated with the mitochondria but also present in the microbodies of dark-grown pea roots and in the glyoxysomes of Ricinus endosperm. In the microbodies isolated from potato tubers the enzyme was not detectable. Glutamate-pyruvate aminotransferase activity was not associated with the proplastid fractions of the non-green tissues. The distribution of glutamate-oxaloacetate aminotransferase was different from that of glutamate-pyruvate aminotransferase. Glutamate-oxaloacetate aminotransferase was found in chloroplasts, proplastids, mitochondria, microbodies and in the supernatant. Evidence is presented that glutamate-pyruvate and glutamate-glyoxylate aminotransferase activities were catalyzed by the same enzyme. Both activities showed the same organelle distribution on sucrose gradients and both were eluted at the same salt concentration from DEAE-cellulose. By chromatography of preparations from rye leaf extracts on DEAE-cellulose two forms of glutamate-pyruvate (glyoxylate) aminotransferase were separated. The major fraction eluting at a low salt concentration was identified as peroxisomal form and the minor fraction eluting at a higher salt concentration was identified as a mitochondrial form. Both the glutamate-glyoxylate and the glutamate-pyruvate aminotransferase activities of the peroxisomal as well as of the mitochondrial forms of the enzyme were strongly (about 80%) inhibited by the presence of 10 mM glycidate, previously described as an inhibitor of glutamate-glyoxylate aminotransferase in tobacco tissue. Pig heart glutamate-pyruvate aminotransferase exhibited no glutamate-glyoxylate aminotransferase activity and was only slightly inhibited by glycidate. The development of glutamate-pyruvate aminotransferase activity in the leaves of rye seedlings was strongly increased in the light, relative to dark-grown seedlings, and very similar to that of catalase activity while the development of glutamate-oxaloacetate aminotransferase was, in close coincidence with the behavior of leaf growth, only slightly enhanced by light. It is discussed that in green leaves an extrachloroplastic synthesis of alanine is of considerable advantage for the metabolic flow during photosynthesis.     

Effects of spatial distribution on plant associational defense against herbivory

Several studies have shown that consumption of a focal plant by herbivores depends not only on its own defense traits but also on the characteristics of the neighboring plants. A number of studies have reported on plant associational defense in relation to neighboring plant palatability but the effect of the spatial distribution of the focal plant within patches of different neighboring plants has received less attention. We conducted a manipulative experiment to determine whether and how spatial distribution of focal plants affects the associational defense between plant species. In our experimental setup sheep encountered two patches varying in spatial distribution of the focal plant within patches (dispersed or clumped) and patch quality, good patch and bad patch, where the focal plant, Lathyrus quinquenervius, was neighbored to high- (Chloris virgata) or low-palatable (Kalimeris integrifolia) species, respectively. Results showed that, when focal plants were dispersed within both patches, the risk of attack was significantly lower for focal plants in the patches with low- than high-palatable neighbors, indicating associational defense. Alternatively, when focal plants were clumped within both patches, they were consumed in bad-patch as much as in good-patch plots, which indicates the absence of associational defense. However, if the focal plants have different spatial distributions in the two patches (dispersed in good-patch and clumped in bad-patch or vice versa), sheep foraging success for focal plants was greatly reduced in dispersed spatial pattern irrespective of the palatability of neighboring plants. Therefore, we concluded that spatial distribution is as important as traits of neighboring plants in predicting vulnerability of the focal plant to grazing by generalist herbivores. The outcome of plant associational defense for different types of neighborhood strongly depends on the magnitude of herbivore foraging selectivity between and within patches, which further depended on the contrasts between plant species or between patches.     

Quantification of the spatial strain distribution of scoliosis using a thin-plate spline method

The objective of this study was to quantify the three-dimensional spatial strain distribution of a scoliotic spine by nonhomogeneous transformation without using a statistically averaged reference spine. The shape of the scoliotic spine was determined from computed tomography images from a female patient with adolescent idiopathic scoliosis. The shape of the scoliotic spine was enclosed in a rectangular grid, and symmetrized using a thin-plate spline method according to the node positions of the grid. The node positions of the grid were determined by numerical optimization to satisfy symmetry. The obtained symmetric spinal shape was enclosed within a new rectangular grid and distorted back to the original scoliotic shape using a thin-plate spline method. The distorted grid was compared to the rectangular grid that surrounded the symmetrical spine. Cobb's angle was reduced from 35° in the scoliotic spine to 7° in the symmetrized spine, and the scoliotic shape was almost fully symmetrized. The scoliotic spine showed a complex Green–Lagrange strain distribution in three dimensions. The vertical and transverse compressive/tensile strains in the frontal plane were consistent with the major scoliotic deformation. The compressive, tensile and shear strains on the convex side of the apical vertebra were opposite to those on the concave side. These results indicate that the proposed method can be used to quantify the three-dimensional spatial strain distribution of a scoliotic spine, and may be useful in quantifying the deformity of scoliosis.     

Refining predictions of climate change impacts on plant species distribution through the use of local statistics

Bioclimate envelope models are often used to predict changes in species distribution arising from changes in climate. These models are typically based on observed correlations between current species distribution and climate data. One limitation of this basic approach is that the relationship modelled is assumed to be constant in space; the analysis is global with the relationship assumed to be spatially stationary. Here, it is shown that by using a local regression analysis, which allows the relationship under study to vary in space, rather than conventional global regression analysis it is possible to increase the accuracy of bioclimate envelope modelling. This is demonstrated for the distribution of Spotted Meddick in Great Britain using data relating to three time periods, including predictions for the 2080s based on two climate change scenarios. Species distribution and climate data were available for two of the time periods studied and this allowed comparison of bioclimate envelope model outputs derived using the local and global regression analyses. For both time periods, the area under the receiver operating characteristics curve derived from the analysis based on local statistics was significantly higher than that from the conventional global analysis; the curve comparisons were also undertaken with an approach that recognised the dependent nature of the data sets compared. Marked differences in the future distribution of the species predicted from the local and global based analyses were evident and highlight a need for further consideration of local issues in modelling ecological variables.     

Integrating ecological features of species in spatial pattern analysis of a plant community

Objective: This empirical study was designed to explore the role of ecological features of species in the spatial patterning of a grassland community. Location: Banks of the river Rhône in France. Material and Methods: First, we explored the spatial pattern of 29 species recorded in the community using spatial autocorrelation analysis of species cover values. Second, we then explored the relationship between the patterns found and a set of life attributes that characterized the ecological features of species for resource foraging or dispersion. Finally, we explored the spatial relationship of groups of species that shared the same ecological features using cross‐correlation analysis. Results: We found a significant relationship between the spatial pattern and life attributes of the species highlighting three groups of species: (1) species characterized as competitors, reproducing by runner clonal organs and forming large, dense patches; (2) species characterized as competitive‐rud‐erals, dispersing exclusively by seed production and forming small periodic patches; and (3) species classified as CSR, characterized by rosette morphology and short rhizomes as clonal organs without any significant spatial autocorrelation. Spatial segregation was found between group 1 and group 2 up to 14 m; no significant cross‐correlation between groups 1 and 3 between 0 and 3.5 m, and association between groups 2 and 3 up to 14 m. Conclusions: These results helped to understand how species attributes (relative to stature or dispersion abilities); external factors (such as disturbance) and biotic processes (competition) interplay in structuring the plant community under study in space.     

Disentangling drivers of spatial autocorrelation in species distribution models

Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e. conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.     

Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions

In ecological modelling, limitations in data and their applicability for predictive modelling are more rule than exception. Often modelling has to be performed on sub-optimal data, as explicit and controlled collection of (more) appropriate data would not be feasible. An example of predictive ecological modelling is given with application of generalized additive and generalized linear models fitted to presence–absence records of plant species and site condition data from four nutrient-poor Flemish lowland valleys. Standard regression procedures are used for modelling, although explanatory and response data do not meet all the assumptions implicit in these procedures. Data were non-randomly collected and are spatially autocorrelated; model residuals retain part of that correlation. The scale of most site-condition records does not match the scale of the response variable (species distribution). Hence, interpolated and up-scaled explanatory variables are used. Data are aggregated from distinct phytogeographical regions to allow for generalized models, applicable to a wider population of river valleys in the same region. Nevertheless, ecologically sound models are obtained, which predict well the distribution of most plant species for the Flemish river valleys considered.     

Elevational distribution of conifer-broadleaved hardwood forests on South Island,New Zealand

Constrained canonical correspondence analysis was used to compare the elevational distribution of conifer-broadleaved hardwood forests at nine localities on South Island, New Zealand. Elevations of individual species were compared using cover-weighted mean elevations and cover-weighted standard deviations of mean elevation. Mean elevations of floristically similar stands declined with latitude, but was also lower at a locality with a granite substrate than at an adjacent locality with a schist substrate. The mean elevation breadth of frequent species (those in >5% of stands) was greatest at a locality underlain by schist and least at a locality underlain by granite. This is consistent with wide habitat breadth for species in early successional stages, because forest underlain by schist is more frequently disturbed than forest underlain by granite. Elevation breadth of frequent species was less, and species' turnover greater, in South Island conifer-broadleaved hardwood forests than in conifer forests at similar latitudes in the Southern Rocky Mountains, USA.     

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.     

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.     

Effects of incorporating spatial autocorrelation into the analysis of species distribution data

Aim  Spatial autocorrelation (SAC) in data, i.e. the higher similarity of closer samples, is a common phenomenon in ecology. SAC is starting to be considered in the analysis of species distribution data, and over the last 10 years several studies have incorporated SAC into statistical models (here termed 'spatial models'). Here, I address the question of whether incorporating SAC affects estimates of model coefficients and inference from statistical models.
Methods  I review ecological studies that compare spatial and non-spatial models.
Results  In all cases coefficient estimates for environmental correlates of species distributions were affected by SAC, leading to a mis-estimation of on average c . 25%. Model fit was also improved by incorporating SAC.
Main conclusions  These biased estimates and incorrect model specifications have implications for predicting species occurrences under changing environmental conditions. Spatial models are therefore required to estimate correctly the effects of environmental drivers on species present distributions, for a statistically unbiased identification of the drivers of distribution, and hence for more accurate forecasts of future distributions.