Exploring the distribution of Sterocorax Ortuño, 1990 (Coleoptera, Carabidae) species in the Iberian Peninsula

Aim The first aim of this paper was to evaluate the distribution of the three Sterocorax species found in the Iberian Peninsula by estimating the main environmental factors that constrain their distributions. The second aim was to explore the potential importance of competitive interactions in limiting their current distributions using predictive distribution models. Location Iberian Peninsula. Methods Species presence data were collected from records in the literature and private and public collections. Ecological niche factor analysis was performed to extract pseudo‐absences (probable absences), which, together with presence data, were modelled using generalized additive models. The models were run twice. Initially we used only environmental variables, and thereafter additional spatial variables were included in order to account for spatially structured factors not accounted for in the environmental variables. Results Highly reliable distribution models were obtained for the three species, with AUC scores (area under the receiver operating characteristics curve) higher than 0.96. The addition of spatial variables to the first model significantly improved the predicted distribution of Corax (Sterocorax) globosus and Corax (Sterocorax) insidiator, by reducing their potential distribution area. In contrast, the model of Corax (Sterocorax) galicianus was not improved by the addition of a spatial term. Main conclusions Generated pseudo‐absences, such as those used in this study, helped to avoid problems of using erroneous data (false absences) in distribution records. Pseudo‐absences greatly improved the models by only selecting absences within the area with the most unfavourable environmental conditions. The importance of spatial variables to both C. (S.) globosus and C. (S.) insidiator distributions probably relates to a number of unknown factors, such as unique historical events. The absence of established populations of C. (S.) globosus north of the Ebro Valley appears to be one such historical factor. The distribution of C. (S.) galicianus only marginally overlaps with that of C. (S.) globosus, according to our environmental factor models. As this overlap is restricted it is not likely to be a result of competitive exclusion; rather, their geographical segregation seems to be environmentally mediated. The addition of spatial variables reduced the potential habitat of C. (S.) insidiator, eliminating some environmentally optimal areas from its distribution. As no environmental barrier seems apparent in this case, competitive interaction with C. (S.) globosus is a plausible hypothesis for its absence in these optimal parts of its range.     

Geographical patterns in prediction errors of species distribution models

Aim To describe and explain geographical patterns of false absence and false presence prediction errors that occur when describing current plant species ranges with species distribution models. Location Europe. Methods We calibrated species distribution models (generalized linear models) using a set of climatic variables and gridded distribution data for 1065 vascular plant species from the Atlas Florae Europaeae. We used randomly selected subsets for each species with a constant prevalence of 0.5, modelled the distribution 1000 times, calculated weighted averages of the model parameters and used these to predict the current distribution in Europe. Using a threshold of 0.5, we derived presence/absence maps. Comparing observed and modelled species distribution, we calculated the false absence rates, i.e. species wrongly modelled as absent, and the false presence rates, i.e. species wrongly modelled as present, on a 50 × 50 km grid. Subsequently, we related both error rates to species range properties, land use and topographic variability within grid cells by means of simultaneous autoregressive models to correct for spatial autocorrelation. Results Grid‐cell‐specific error rates were not evenly distributed across Europe. The mean false absence rate was 0.16 ± 0.12 (standard deviation) and the mean false presence rate was 0.22 ± 0.13. False absence rates were highest in central Spain, the Alps and parts of south‐eastern Europe, while false presence rates were highest in northern Spain, France, Italy and south‐eastern Europe. False absence rates were high when range edges of species accumulated within a grid cell and when the intensity of human land use was high. False presence rates were positively associated with relative occurrence area and accumulation of range edges. Main conclusions Predictions for various species are not only accompanied by species‐specific but also by grid‐cell‐specific errors. The latter are associated with characteristics of the grid cells but also with range characteristics of occurring species. Uncertainties of predictive species distribution models are not equally distributed in space, and we would recommend accompanying maps of predicted distributions with a graphical representation of predictive performance.     

Climate-based models of spatial patterns of species richness in Egypt's butterfly and mammal fauna

Aim  Identifying areas of high species richness is an important goal of conservation biogeography. In this study we compared alternative methods for generating climate-based estimates of spatial patterns of butterfly and mammal species richness.
Location  Egypt.
Methods  Data on the occurrence of butterflies and mammals in Egypt were taken from an electronic database compiled from museum records and the literature. Using M axent , species distribution models were built with these data and with variables describing climate and habitat. Species richness predictions were made by summing distribution models for individual species and by modelling observed species richness directly using the same environmental variables.
Results  Estimates of species richness from both methods correlated positively with each other and with observed species richness. Protected areas had higher species richness (both predicted and actual) than unprotected areas.
Main conclusions  Our results suggest that climate-based models of species richness could provide a rapid method for selecting potential areas for protection and thus have important implications for biodiversity conservation.     

Habitat suitability modelling of an invasive plant with advanced remote sensing data

Aim  Lepidium latifolium (Brassicaceae; perennial pepperweed) is a noxious Eurasian weed invading riparian and wetland areas of the western USA. Understanding which sites are most susceptible to invasion by L. latifolium will allow more efficient management of this weed. We assessed the ability of advanced remote sensing techniques to develop habitat suitability models for L. latifolium .
Location  San Francisco Bay/Sacramento-San Joaquin River Delta, California, USA.
Methods  Lepidium latifolium distribution was mapped with hyperspectral image data of Rush Ranch Open Space Preserve, providing presence/absence data to train and validate habitat models. A high-resolution light detection and ranging digital elevation model was used to derive predictor environmental variables (distance to channel, distance to upland, elevation, slope, aspect and convexity). Aggregate decision tree models were used to predict the potential distribution of this species.
Results  Lepidium latifolium infested two zones: near the marshland–upland margin and along channels within the marsh. Topographical data, which are typically strongly correlated with wetland species distributions, were relatively unimportant to L. latifolium occurrence, although relevant microtopography information, particularly relative elevation, was subsumed in the distance to channel variable. The map of potential L. latifolium distribution reveals that Rush Ranch contains considerable habitat that it is susceptible to continued invasion.
Main conclusions  Lepidium latifolium invades relatively less stressful sites along the inundation and salinity gradients. Advanced remote sensing datasets were shown to be sufficient for species distribution modelling. Remote sensing offers powerful tools that deserve wider use in ecological research and management.     

Ecological niche modelling of an invasive alien plant and its potential biological control agents

Invasive alien plants are of concern in South Africa. Pompom weed (Campuloclinium macrocephalum) is currently invading the Grassland and Savannah biomes of South Africa and is likely to continue spreading in the southern African sub- region. Two possible biological control agents (Liothrips tractabilis and Cochylis campuloclinium) have been identified for control of pompom weed. We used ecological niche modelling to predict which areas in southern Africa are likely to be suitable for pompom weed and the two potential biological control agents. The overlap between areas predicted to be highly suitable for pompom weed and areas suitable for the biological control agents was assessed. Methods of reducing sampling bias in a data set used for calibrating models were also compared. Finally, the performance of models calibrated using only native range data, only invaded range data and both were also compared. Models indicate that pompom weed is likely to spread across a greater region of southern Africa than it currently occupies, with the Savannah and Grassland biomes being at greatest risk of invasion. Poor overlap was found between the areas predicted to be highly suitable for pompom weed and those areas predicted to be suitable for the biological control agents. However, models of the potential distribution of the biological control agents are interpreted with caution due to the very small sample size of the data set used to calibrate the models. Models calibrated using both native range and invaded range data were found to perform best whilst models calibrated using only native range data performed the worst. There was little difference found between models that were calibrated using spatially reduced (selecting only one record per 30 min grid cell) and randomly reduced (randomly selecting 50% of available records) biased data sets.     

Detecting the influence of climatic variables on species distributions: a test using GIS niche-based models along a steep longitudinal environmental gradient

Aim  To investigate the influence of climate variables in shaping species distributions across a steep longitudinal environmental gradient.
Location  The state of Oklahoma, south-central United States.
Methods  We used Geographical Information Systems (GIS) niche-based models to predict the geographic distributions of six pairs of closely related amphibian and reptile species across a steep longitudinal environmental gradient. We compared results from modelling with actual distributions to determine whether species distributions were primarily limited by environmental factors, and to assess the potential roles of competition and historical factors in influencing distributions.
Results  For all species pairs, GIS models predicted an overlap zone in which both species should occur, although in reality in some cases this area was occupied by only one of the species. We found that environmental factors clearly influence the distributions of most species pairs. We also found evidence suggesting that competition and evolutionary history play a role in determining the distributions of some species pairs.
Main conclusions  Niche-based GIS modelling is a useful tool for investigating species distribution patterns and the factors affecting them. Our results showed that environmental factors strongly influenced species distributions, and that competition and historical factors may also be involved in some cases. Furthermore, results suggested additional lines of research, such as ecological comparisons among populations occurring inside and outside predicted overlap zones, which may provide more direct insight into the roles of competitive interactions and historical factors in shaping species distributions.     

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.     

Comparison of methods for estimating the spread of a non-indigenous species

Aim  To compare different quantitative approaches for estimating rates of spread in the exotic species gypsy moth, Lymantria dispar L., using county-level presence/absence data and spatially extensive trapping grids.
Location  USA
Methods  We used county-level presence/absence records of the gypsy moth's distribution in the USA, which are available beginning in 1900, and extensive grids of pheromone-baited traps, which are available in selected areas beginning in 1981. We compared a regression approach and a boundary displacement approach for estimating gypsy moth spread based on these sources of data.
Results  We observed relative congruence between methods and data sources in estimating overall rates of gypsy moth spread through time, and among regions.
Main conclusions  The ability to estimate spread in exotic invasive species is a primary concern in management programmes and one for which there is a lack of information on the reliability of methods. Also, in most invading species, there is generally a lack of data to explore methods of estimating spread. Extensive data available on gypsy moth in the USA allowed for such a comparison. We show that, even with spatially crude records of presence/absence, overall rates of spread do not differ substantially from estimates obtained from the more costly deployment of extensive trapping grids. Moreover, these methods can also be applied to the general study of species distributional changes, such as range expansion or retraction, in response to climate change or other environmental effects.     

Current distribution and potential extent of the most invasive alien plant species on La Réunion (Indian Ocean,Mascarene islands)

Abstract La Réunion Island has the largest area of intact vegetation of the islands in the Mascarene archipelago. Biological invasions are the primary threat to biodiversity in the intact habitats of the island (those not already transformed by agriculture and urbanization). Our study aimed to identify areas to prioritize in managing invasive alien plants for biodiversity conservation. We used extensive surveys of 238 distinct untransformed areas on La Réunion to define the current distribution patterns of all invasive species. Using expert knowledge, we compiled maps of the current distribution of the 46 most widespread/important invasive plants at the habitat scale (identified according to vegetation structure). Data from 440 botanical relevés for the 20 most threatening invasive alien plant species across the island and climatic envelope models were used to derive climatic suitability surfaces; these were used to map potential distributions for these species. More than 10 species invade 16.7% of the remaining habitat. Five habitat types are invaded by 25 or more species, and eight have fewer than 10 invasive alien plant species. Cluster analysis based on presence/absence of species in the 18 habitat types produced eight groups of species that invade particular habitats. Potential distribution models show that some species have invaded large parts of their potential range (e.g. Fuchsia magellanica, Furcraea foetida, Hiptage benghalensis), whereas others have the potential to increase their range substantially (e.g. Clidemia hirta, Strobilanthes hamiltonianus, Ulex europaeus). Management implications are identified for both groups. Three broad groups of habitats were identified: (i) intact habitats with a low level of invasion (e.g. subalpine shrubland); (ii) moderately invaded habitats with varying levels of intactness (ranging from windward submountain rainforest to the Acacia heterophylla forest); and (iii) habitats with little remaining intact area and high levels of invasion (e.g. lowland rainforest). Different management interventions are appropriate for these three groups.     

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola’s surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.     

Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models

Aim Niche‐based distribution models are often used to predict the spread of invasive species. These models assume niche conservation during invasion, but invasive species can have different requirements from populations in their native range for many reasons, including niche evolution. I used distribution modelling to investigate niche conservatism for the Asian tiger mosquito (Aedes albopictus Skuse) during its invasion of three continents. I also used this approach to predict areas at risk of invasion from propagules originating from invasive populations. Location Models were created for Southeast Asia, North and South America, and Europe. Methods I used maximum entropy (Maxent ) to create distribution models using occurrence data and 18 environmental datasets. One native model was created for Southeast Asia; this model was projected onto North America, South America and Europe. Three models were created independently for the non‐native ranges and projected onto the native range. Niche overlap between native and non‐native predictions was evaluated by comparing probability surfaces between models using real data and random models generated using a permutation approach. Results The native model failed to predict an entire region of occurrences in South America, approximately 20% of occurrences in North America and nearly all Italian occurrences of A. albopictus. Non‐native models poorly predict the native range, but predict additional areas at risk for invasion globally. Niche overlap metrics indicate that non‐native distributions are more similar to the native niche than a random prediction, but they are not equivalent. Multivariate analyses support modelled differences in niche characteristics among continents, and reveal important variables explaining these differences. Main conclusions The niche of A. albopictus has shifted on invaded continents relative to its native range (Southeast Asia). Statistical comparisons reveal that the niche for introduced distributions is not equivalent to the native niche. Furthermore, reciprocal models highlight the importance of controlling bi‐directional dispersal between native and non‐native distributions.     

中国重要检疫性实蝇的全球分布和入侵情况

实蝇是重要的蔬菜瓜果害虫。本文重点概述入侵中国实蝇的寄主植物、原产地、首次发现时间、入侵地、国内外分布和入侵扩散途径,全面整理重要检疫性实蝇的全球分布和入侵情况,同时根据全球重要检疫性实蝇的分布情况为警惕和预防实蝇入侵我国提供理论支持。本文指出,领土相邻是实蝇入侵我国的重要条件：入侵中国的实蝇原产地是中国的邻国日本、印度和泰国,最早发现地是中国的沿海和沿边地区,分别为中国台湾、广西、云南和新疆吐鲁番。寄主植物的种类数量与实蝇的分布范围正相关：橘小实蝇和瓜实蝇是多食性昆虫,分别危害305和61种植物,分别在全球83和58个国家均有发生。枣实蝇、蜜柑大实蝇和黑颜面实蝇是寡食性昆虫,只危害同属植物,仅入侵原产地周边的些许国家。随着国际瓜果贸易交流频繁,全球重要的检疫实蝇由原产地国家逐渐向周边国家扩散,其中油橄榄实蝇、桃果实蝇、埃塞尔比亚寡鬓实蝇、黑樱桃实蝇和苹果实蝇已经入侵至我国周边国家,需要警惕和预防其从沿边省份入侵我国。加强检疫和管理是防治实蝇入侵和扩散的重要手段,同时也需要中国的科研工作者进一步对世界上重大检疫实蝇的入侵风险评估、防控对策、分子鉴定和快速检测方法进行研究。     

The value of georeferenced collection records for predicting patterns of mosquito species richness and endemism in the Neotropics

Abstract.  1. Determining large-scale distribution patterns for mosquitoes could advance knowledge of global mosquito biogeography and inform decisions about where mosquito inventory needs are greatest.
2. Over 43 000 georeferenced records are presented of identified and vouchered mosquitoes from collections undertaken between 1899 and 1982, from 1853 locations in 42 countries throughout the Neotropics. Of 492 species in the data set, 23% were only recorded from one location, and Anopheles albimanus Wiedemann is the most common species.
3. A linear log–log species–area relationship was found for mosquito species number and country area. Chile had the lowest relative density of species and Trinidad-Tobago the highest, followed by Panama and French Guiana.
4. The potential distribution of species was predicted using an Ecological Niche Modelling (ENM) approach. Anopheles species had the largest predicted species ranges, whereas species of Deinocerites and Wyeomyia had the smallest.
5. Species richness was estimated for 1° grids and by summing predicted presence of species from ENM. These methods both showed areas of high species richness in French Guiana, Panama, Trinidad-Tobago, and Colombia. Potential hotspots in endemicity included unsampled areas in Panama, French Guiana, Colombia, Belize, Venezuela, and Brazil.
6. Argentina, The Bahamas, Bermuda, Bolivia, Cuba, and Peru were the most under-represented countries in the database compared with known country species occurrence data. Analysis of species accumulation curves suggested patchiness in the distribution of data points, which may affect estimates of species richness.
7. The data set is a first step towards the development of a global-scale repository of georeferenced mosquito collection records.     

Simulating the effects of climate change on the distribution of an invasive plant,using a high resolution,local scale,mechanistic approach: challenges and insights

The growing economic and ecological damage associated with biological invasions, which will likely be exacerbated by climate change, necessitates improved projections of invasive spread. Generally, potential changes in species distribution are investigated using climate envelope models; however, the reliability of such models has been questioned and they are not suitable for use at local scales. At this scale, mechanistic models are more appropriate. This paper discusses some key requirements for mechanistic models and utilises a newly developed model (PSS[gt]) that incorporates the influence of habitat type and related features (e.g., roads and rivers), as well as demographic processes and propagule dispersal dynamics, to model climate induced changes in the distribution of an invasive plant (Gunnera tinctoria) at a local scale. A new methodology is introduced, dynamic baseline benchmarking, which distinguishes climate‐induced alterations in species distributions from other potential drivers of change. Using this approach, it was concluded that climate change, based on IPCC and C4i projections, has the potential to increase the spread‐rate and intensity of G. tinctoria invasions. Increases in the number of individuals were primarily due to intensification of invasion in areas already invaded or in areas projected to be invaded in the dynamic baseline scenario. Temperature had the largest influence on changes in plant distributions. Water availability also had a large influence and introduced the most uncertainty in the projections. Additionally, due to the difficulties of parameterising models such as this, the process has been streamlined by utilising methods for estimating unknown variables and selecting only essential parameters.     

Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity

Interactions between climate change and non-native invasive species may combine to increase invasion risk to native ecosystems. Changing climate creates risk as new terrain becomes climatically suitable for invasion. However, climate change may also create opportunities for ecosystem restoration on invaded lands that become climatically unsuitable for invasive species. Here, I develop a bioclimatic envelope model for cheatgrass ( Bromus tectorum ), a non-native invasive grass in the western US, based on its invaded distribution. The bioclimatic envelope model is based on the Mahalanobis distance using the climate variables that best constrain the species' distribution. Of the precipitation and temperature variables measured, the best predictors of cheatgrass are summer, annual, and spring precipitation, followed by winter temperature. I perform a sensitivity analysis on potential cheatgrass distributions using the projections of 10 commonly used atmosphere–ocean general circulation models (AOGCMs) for 2100. The AOGCM projections for precipitation vary considerably, increasing uncertainty in the assessment of invasion risk. Decreased precipitation, particularly in the summer, causes an expansion of suitable land area by up to 45%, elevating invasion risk in parts of Montana, Wyoming, Utah, and Colorado. Conversely, increased precipitation reduces habitat by as much as 70%, decreasing invasion risk. The strong influence of precipitation conditions on this species' distribution suggests that relying on temperature change alone to project future change in plant distributions may be inadequate. A sensitivity analysis provides a framework for identifying key climate variables that may limit invasion, and for assessing invasion risk and restoration opportunities with climate change.     

Predicting the distribution potential of an invasive frog using remotely sensed data in Hawaii

Aim Eleutherodactylus coqui (commonly known as the coqui) is a frog species native to Puerto Rico and non‐native in Hawaii. Despite its ecological and economic impacts, its potential range in Hawaii is unknown, making control and management efforts difficult. Here, we predicted the distribution potential of the coqui on the island of Hawaii. Location Puerto Rico and Hawaii. Methods We predicted its potential distribution in Hawaii using five biophysical variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) as predictors, presence/absence data collected from Puerto Rico and Hawaii and three classification methods – Classification Trees (CT), Random Forests (RF) and Support Vector Machines (SVM). Results Models developed separately using data from the native range and the invaded range predicted potential coqui habitats in Hawaii with high performance. Across the three classification methods, mean area under the ROC curve (AUC) was 0.75 for models trained using the native range data and 0.88 for models trained using the invaded range data. We achieved the highest AUC value of 0.90 using RF for models trained with invaded range data. Main conclusions Our results showed that the potential distribution of coquis on the island of Hawaii is much larger than its current distribution, with RF predicting up to 49% of the island as suitable coqui habitat. Predictions also show that most areas with an elevation between 0 and 2000 m are suitable coqui habitats, whereas the cool and dry high elevation areas beyond 2000 m elevation are unsuitable. Results show that MODIS‐derived biophysical variables are capable of characterizing coqui habitats in Hawaii.     

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.     

Predicting spread of invasive macrophytes in New Zealand lakes using indirect measures of human accessibility

1. Models predicting invasive macrophyte spread between lakes provide an important tool for focusing proactive management efforts to lakes deemed susceptible to invasion. However, challenges to forecasting macrophyte spread include wide physiological tolerances of invasive macrophytes and a lack of information on the relative importance of the various human vectors (e.g. boating traffic). In New Zealand, three invasive species that reproduce vegetatively, Ceratophyllum demersum, Lagarosiphon major, Egeria densa, and a single species that reproduces sexually, Utricularia gibba, are currently spreading across the lake landscape at a great cost to the local ecology and economy. 2. In this study, we first examined whether variables that indirectly describe weed spread via human access and use, as well as a lake’s position in the landscape, could describe the distribution of these four species using a boosted regression trees (BRT) modelling approach. Then, as these invasive species have not reached their full invasion potential, we examined how giving more influence to infected lakes at the edge of the invasion front, and including all lakes across New Zealand as background samples, simulating ‘absences beyond the invasion front’, influenced our ability to forecast the potential for new lakes to be invaded. 3. The BRT models identified that variables characterising human access and use, as well as lake position, were associated with the occurrence of the three vegetatively reproducing macrophytes. Weed occurrence was more likely when there was a highway in the vicinity, human population density was high and if the lake was large (c. 55 km²). But in the single case of U. gibba, temperature was the variable that best explained occurrence. This is consistent with the suggestion that U. gibba is predominantly dispersed by waterbirds, rather than human activity. 4. But for all four species, the BRT models based on the recorded observations alone predicted observed invasions with low prediction probabilities and did not forecast further spread. By contrast, when observations at the edge of the invasion front were upweighted, and additional background lakes implemented into the model, recorded observations were predicted and additional lakes were forecast to be at risk, suggesting that these models better captured the current and potential distribution of these macrophyte species. 5. The use of variables that characterise weed spread could provide similar insights into other systems where survey information on the nature, strength and direction of invasion vectors is lacking. Furthermore, when weighting the data, many lakes across New Zealand were forecasted to be at risk of invasion. The advantage of weighing the presence data was that insights into the potential for a species to spread were obtained. The probabilistic estimates of risk, as derived from the models, together with other information for prioritising lakes, can be used to focus surveillance and protection efforts.     

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。     

Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar

Aim Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available. Location Madagascar. Methods Models were developed and evaluated for 13 species of secretive leaf‐tailed geckos (Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km² grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP). Results We found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included. Main conclusions We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species.