Predicting global change impacts on plant species’ distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species’ range shifts, changes in phenology and species’ extinctions, accurate projections of species’ responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species’ responses to future environmental changes.

There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species’ distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the “trailing edge” of shifting populations, species’ interactions and the interaction between the effects of climate and land-use.

In this review, we propose two main avenues to progress the understanding and prediction of the different processes occurring on the leading and trailing edge of the species’ distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species’ migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species’ distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world.     

Do lichens show latitudinal patterns of diversity?

No previous study has directly investigated whether lichens show latitudinal patterns of diversity. We used vouchered data and MaxEnt models to compile richness estimates (species, genera, and families) across the western coastal region of the US. Nonparametric multiplicative regression then sought the geographic factors or interactions of factors that explained the most variability in lichen richness. Collection density was the strongest predictor of raw estimates of richness at all taxonomic ranks. Latitude was the overall single-best predictor of MaxEnt modeled species, generic, and familial richness in all models. MaxEnt modeling was necessary to minimize collection bias, which otherwise obscured any other patterns of diversity. While geography explained a sizable portion of variance in lichen richness, it does not trend linearly with latitude. Instead, lichen diversity may be influenced by a compilation of regional and local factors including climate, disturbance, and competition.     

基于MaxEnt和ArcGIS模拟檫木分布格局及其栖息地的变化

气候变化是影响物种分布和多样性的主要原因之一,越来越受到人们的关注。本研究从世界气候网站下载了19个气候因子数据,通过网上查阅和实地考察获取檫木(Sassafras tzumu Hemsl.)分布数据共233个,使用ArcGIS 10.2和MaxEnt 3.3.2对檫木不同时期分布格局进行模拟,推测檫木末次冰期和2070年分布格局。研究结果显示,檫木当前分布主要受最干季度降水量、最湿月降水量、温度季节变化和最湿季度平均温度影响。此外,横断山脉、武夷山、天目山和大巴山周边是檫木末次冰期的4个主要分布地区。对当前和2070年模拟结果表明,檫木的适生区整体缩小并向北方移动。表明随着当前气候变化及工业快速发展,在短短几十年时间内对檫木分布格局的影响与过去两万年的相当。     

The Influence of the Incubation Period on Qualitative Aspects of the Spread of AIDS

Based on a simple stochastic model the influence of the incubation period of the HIV-infection on some qualitative aspects of the spread of AIDS is studied. A critical ‘nfection rat’ is calculated in dependence on parameters of the incubation period density function.     

Incubation temperature fluctuation does not affect incubation length and hatchling phenotype in the Chinese skink Plestiodon chinensis

Studies examining the effects of incubation temperature fluctuation on the phenotype of hatchling reptiles have shown species variation. To examine whether incubation temperature fluctuation has a key role in influencing the phenotype of hatchling Chinese skinks (Plestiodon chinensis), we incubated eggs produced by 20 females under five thermal regimes (treatments). Eggs in three treatments were incubated in three incubators, one set constant at 27 °C and two ramp-programmed at 27±3 °C and 27±5 °C on a cycle of 12 h (+) and 12 h (−). The remaining eggs were incubated in two chambers: one inside a room where temperatures varied from 23.0 to 31.1 °C, with a mean of 27.0 °C; the other outside the room where temperatures varied from 20.2 to 35.3 °C, with a mean of 26.1 °C. We found that: (1) for eggs at a given embryonic stage at ovipositon, the mean rather than the variance of incubation temperatures determined the length of incubation; (2) most (egg mass, embryonic stage at oviposition, incubation length and all examined hatchling traits except tail length and locomotor performance) of the examined variables were affected by clutch; and (3) body mass was the only hatchling trait that differed among the five treatments, but the differences were tiny. These findings suggest that incubation temperature fluctuation has no direct role in influencing incubation length and hatchling phenotype in P. chinensis.     

Predicting and mapping the spatial distribution of Chamaecyparis formosensis in central Taiwan in a GIS with species distribution models

Analyzing the relationship between species and environment is always a focal question of ecological research. In recent years species distribution models (SDMs) has been widely used to predict the spatial distribution of species. SDMs are numerical tools that combine observations and species occurrence or abundance with environmental variables to predict the spatial distribution of species across landscapes, sometimes requiring extrapolation in space and time. Chamaecyparis formosensis (Taiwan red cypress, TRCs) is a coniferous species endemic to Taiwan, where it natural grows in the central mountains at moderate to high altitudes of 800–2800 m, and most stands in the range of 1500–2150 m. It is threatened by habitat loss and over-cutting for its valuable timber. To preserve TRCs species and achieve sustainable use of biological resources, we choose TRCs as a target for the study to predict its distribution in central Taiwan.The pure forests of TRCs in the study area were mainly located in Pachsienshan (P), Shouchentashan (S) and Baigou Mountain (B) in central Taiwan, and the distribution data were originally obtained by The Third Survey of Forest Resources and Land Use in Taiwan. Elevation, slope, aspect, and three vegetation indices were derived from both SPOT-5 satellite images and DEM. GIS technique was used to overlay those factors. Discriminant analysis (DA), decision tree (DT) and maximum entropy (MAXENT), three commonly used SDMs, were applied based on above-mentioned six variables to predict the suitable habitat of TRCs, and to evaluate which the best model is in terms of accuracy and efficiency. Three experiment designs (ED1, ED2 and ED3) with different combinations of samples were used for model building and validation. The 200 target samples were collected from the site P–B, B–S and P–S for model building under ED1, ED2 and ED3 respectively, while the 100 samples were collected from the site S, P and B for model validation. All experiment designs had same 1350 background samples. The results showed that the overall accuracy and kappa coefficient of DT (96%, 0.88) was higher than that of MAXENT (91%, 0.70), and their accuracies were better than that of DA (84%, 0.58). All the three models were highly efficient in implementation of model construction and evaluation, while the DT model was difficult for generating the entire predicted map of potential habitat due to its complex conditional sentence. Vegetation indices derived from SPOT-5 satellite images could not improve model accuracy because of its insufficiency of spectral resolution and spatial resolution. High spatial resolution and spectral resolution remotely sensed imagery should be used in our future research to improve model performance and reliability.     

基于MaxEnt模型模拟肯尼亚茜草科河骨木属植物的潜在分布及其在植物志中的应用初探

物种分布模型目前被广泛应用于生物学、生态学和保护生物学的各个领域。该文以肯尼亚茜草科河骨木属（Afrocanthium ）为例,利用最大熵模型（MaxEnt）模拟植物在当前气候情景下的潜在分布,并将这些分布图利用于正在编写的《肯尼亚植物志》中。结果显示,基于足够的原始标本记录,模型能够很好地模拟出每种植物的潜在分布区域。相比传统和新一代植物志仅提供标本信息点或是粗略分布图,《肯尼亚植物志》预采用的潜在分布图,将为志书使用者提供更加全面、实用的信息。     

Do temperature fluctuations during incubation always play an important role in shaping the phenotype of hatchling reptiles?

Fluctuating temperatures (FTs) influence hatchling phenotypes differently from constant temperatures (CTs) in some reptiles, but not in others. This inconsistency raises a question of whether thermal fluctuations during incubation always play an important role in shaping the phenotype of hatchlings. To answer this question, we incubated eggs of Naja atra under one CT (28 °C, CT), two temperature-shift [cold first (CF) and hot first (HF) in which eggs were first incubated at 24 or 32 °C and then at the other, each for 20 days, and finally at 28 °C until hatching], and one FT thermal regimes. Female hatchlings were larger in snout–vent length but smaller in tail length, head size than male hatchlings from the same-sized egg; female hatchlings had more ventral scales than did male hatchlings. The FT and HF treatments resulted in shorter incubation lengths. Tail length was greatest in the CT treatment and smallest in the FT treatment, with the CF and HF treatments in between; head width was greater in the CT treatment than in the other three treatments. Other examined hatchling traits did not differ among treatments. The observed morphological modifications cannot be attributed to the effect of thermal fluctuations but to the effect of temperatures close to the upper and lower viable limits for the species. Our results therefore support the hypothesis that hatchling phenotype is not altered by thermal fluctuation in species with no phenotypic response to incubation temperature within some thresholds.     

Assessing the applicability of fuzzy neural networks for habitat preference evaluation of Japanese medaka (Oryzias latipes)

Species–environment relationships are key information for the development of planning and management strategies for conservation or restoration of ecosystems. Artificial neural networks (ANNs) are one widely applied type of species distribution model (SDM). Fuzzy neural networks (FNNs), that is, fuzzified ANNs, have been introduced to take into account the uncertainties inherent in fish behaviour and errors in input data. Despite their high predictive ability in modelling complex systems, FNNs cannot describe habitat preference curves (HPCs), although these are the basis for habitat quality assessment. The present study therefore aimed to evaluate the applicability of FNNs for modelling habitat preference and spatial distributions of Japanese medaka (Oryzias latipes), one of the most common freshwater fish in Japan. Three independent data sets were collected during a series of field surveys and used for model development and evaluation of FNNs. A weight decay backpropagation algorithm was additionally introduced, and its effects on the FNNs were evaluated on the basis of model performance and habitat preference information retrieved from the field observation data. Modified sensitivity analysis was applied to derive HPCs of the target fish. Application of weight decay backpropagation markedly reduced the variability of the model structures, improved the generalization ability of the FNNs, and resulted in well-converged and consistent HPCs that were similar to those evaluated by fuzzy habitat preference models. These results support the applicability of FNNs to habitat preference modelling, which can provide useful information on the habitat use by the target fish. Further study should focus on the effects of sources of uncertainty, such as zero abundance, on the SDMs and the resulting habitat preference evaluation.     

Patterns in the co-occurrence of fish species in streams: the role of site suitability,morphology and phylogeny versus species interactions

A number of studies at large scales have pointed out that abiotic factors and recolonization dynamics appear to be more important than biotic interactions in structuring stream-fish assemblages. In contrast, experimental and field studies at small scales show the importance of competition among stream fishes. However, given the highly variable nature of stream systems over time, competition may not be intense enough to generate large-scale complementary distributions via competitive exclusion. Complementary distribution is a recurrent pattern observed in fish communities across stream gradients, though it is not clear which instances of this pattern are due to competitive interactions and which to individual species requirements. In this study, I introduce a series of null models developed to provide a more robust evaluation of species associations by facilitating the distinction between different processes that may shape species distributions and community assembly. These null models were applied to test whether conspicuous patterns in species co-occurrences are more consistent with their differences in habitat use, morphological features and/or phylogenetic constraints, or with species interactions in fish communities in the streams of a watershed in eastern Brazil. I concluded that patterns in species co-occurrences within the studied system are driven by common species-habitat relationships and species interactions may not play a significant role in structuring these communities. I suggest that large-scale studies, where adequate designs and robust analytical tools are applied, can contribute substantially to understanding the importance of different types of processes in structuring stream-fish communities.     

Predicting species distributions in poorly-studied landscapes

Conservationists are increasingly relying on distribution models to predict where species are likely to occur, especially in poorly-surveyed but biodiverse areas. Modeling is challenging in these cases because locality data necessary for model formation are often scarce and spatially imprecise. To identify methods best suited to modeling in these conditions, we compared the success of three algorithms (Maxent, Mahalanobis Typicalities and Random Forests) at predicting distributions of eight bird and eight mammal species endemic to the eastern slopes of the central Andes. We selected study species to have a range of locality sample sizes representative of the data available for endemic species of this region and also that vary in their distribution characteristics. We found that for species that are known from moderate numbers (N = 38–94) of localities, the three methods performed similarly for species with restricted distributions but Maxent and Random Forests yielded better results for species with wider distributions. For species with small numbers of sample localities (N = 5–21), Maxent produced the most consistently successful results, followed by Random Forests and then Mahalanobis Typicalities. Because evaluation statistics for models derived from few localities can be suspect due to the poor spatial representation of the evaluation data, we corroborated these results with review by scientists familiar with the species in the field. Overall, Maxent appears to be the most capable method for modeling distributions of Andean bird and mammal species because of the consistency of results in varying conditions, although the other methods have strengths in certain situations. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.