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Threatened Fabaceae taxa in coastal East Africa: Current and future modelled distributions and conservation priorities
Abstract
Predicting the responses of threatened tropical plant species to global climate change has been considered to be critical for assessing changes in species distribution and evaluating their conservation status. In reflecting on the vast species richness, East Africa has long been recognised as a hotspot of biodiversity, but very little is known about the vulnerability of the endemic plant diversity to anthropogenic introduced climate changes. This study evaluated the potential impacts of global climate change on plant species ranges in coastal East Africa by predicting the extent and direction of projected changes in climatic suitability. Specifically , we employed species distribution modelling in MaxEnt to identify species experiencing the highest threat of range declines. To do so, we evaluated climatic suitability for eleven legume species using one global climate model and two greenhouse gas emissions scenarios for present and future climates. The findings indicated that the mean AUC and TSS values of the focal taxa ranged from 0.818 to 0.992 and from 0.780 to 0.851, respectively, indicating that the MaxEnt model's prediction accuracy was good or exceptional. Occupancy and abundance of nine species were positively associated with low elevations, high relative humidity, and warmer temperatures in the coastal regions. Regardless of species, precipitation of the warmest quarter and mean temperature of the wettest quarter exhibited a minor impact on the distribution. Furthermore, the probable distribution regions of these species ranged from 77,270 km 2 to 282,297 km 2. To our knowledge, this study is the first to appraise the distribution of threatened species within Fabaceae in coastal East Africa. The current findings provide a critical assessment framework for the conservation and management of Faba-ceae in the region.
... Following that, the best models for ensemble modelling were chosen based on Kappa values and the Area Under the Receiver Operator Curve (AUC). The accuracy of the models was evaluated using the area under the receiver operating characteristic (AUC; Area under the ROC Curve) which is a nonparametric rating tool for the model's ability to predict both presence and absence (sensitivity and specificity; Ngarega et al. 2022). AUC values range from 0 to 1, and the closer an AUC value is to 1, the more effective the model is (Swets 1988: Table 1). ...
An effective method for identifying species and evaluating the effects of changes caused by humans on specific species is the application of species distribution modelling (SDM) in desert environments. The fact that many dry lands and deserts throughout the world are situated in inhospitable regions may be the reason why such applications are still infrequently used on plant species in Egypt's Mediterranean region. Henceforth, the current study aims to map species richness and weighted endemism of Mediterranean endemics in the Mareotis subsector in Egypt and determine the environmental variables influencing distribution of these taxa. We produced a map of species distribution range using Ensemble SDMs. Further, stacked machine learning ensemble models derived from Random Forest (RF) and MaxEnt models were applied on 382 Mediter-ranean endemics distribution data to estimate and map diversity and endemism using two indices: species richness (SR) and weighted endemism index (WEI). The best models for ensemble modelling were chosen based on Kappa values and the Area Under the Receiver Operator Curve (AUC). The results showed that the models had a good predictive ability (Area Under the Curve (AUC) for all SDMs was > 0.75), indicating high accuracy in forecasting the potential geographic distribution of Mediterranean endemics. The main bioclimatic variables that impacted potential distributions of most species were wind speed, elevation and minimum temperature of coldest month. According to our models, six hotspots were determined for Mediterranean endemics in the present study. The highest species richness was recorded in Sallum, Matrouh wadis and Omayed, followed by Burg El-Arab, Ras El-Hekma and Lake Mariut. Indeed, species richness and endemism hotspots are promising areas for conservation planning. This study can help shape policy and mitigation efforts to protect and preserve Mediterranean endemics in the coastal desert of Egypt. These hotspots should be focused on by policy makers and stake-holders and declared as protectorates in the region. The largest number of species per area would be protected by focusing primarily on the hotspots with high species richness.
Climate change poses a serious long-term threat to biodiversity. To effectively reduce biodiversity loss, conservationists need to have a thorough understanding of the preferred habitats of species and the variables that affect their distribution. Therefore, predicting the impact of climate change on species-appropriate habitats may help mitigate the potential threats to biodiversity distribution. Xerophyta, a monocotyledonous genus of the family Velloziaceae is native to mainland Africa, Madagascar, and the Arabian Peninsula. The key drivers of Xerophyta habitat distribution and preference are unknown. Using 308 species occurrence data and eight environmental variables, the MaxEnt model was used to determine the potential distribution of six Xerophyta species in Africa under past, current and future climate change scenarios. The results showed that the models had a good predictive ability (Area Under the Curve and True Skill Statistics values for all SDMs were more than 0.902), indicating high accuracy in forecasting the potential geographic distribution of Xerophyta species. The main bioclimatic variables that impacted potential distributions of most Xerophyta species were mean temperature of the driest quarter (Bio9) and precipitation of the warmest quarter (Bio18). According to our models, tropical Africa has zones of moderate and high suitability for Xerophyta taxa, which is consistent with the majority of documented species localities. The habitat suitability of the existing range of the Xerophyta species varied based on the climate scenario, with most species experiencing a range loss greater than the range gain regardless of the climate scenario. The projected spatiotemporal patterns of Xerophyta species help guide recommendations for conservation efforts.
Understanding the influence of environmental covariates on plant distribution is critical, especially for aquatic plant species. Climate change is likely to alter the distribution of aquatic species. However, knowledge of this change on the burden of aquatic macroorganisms is often fraught with difficulty. Ottelia, a model genus for studying the evolution of the aquatic family Hydrocharitaceae, is mainly distributed in slow-flowing creeks, rivers, or lakes throughout pantropical regions in the world. Due to recent rapid climate changes, natural Ottelia populations have declined significantly. By modeling the effects of climate change on the distribution of Ottelia species and assessing the degree of niche similarity, we sought to identify high suitability regions and help formulate conservation strategies. The models use known background points to determine how environmental covariates vary spatially and produce continental maps of the distribution of the Ottelia species in Africa. Additionally, we estimated the possible influences of the optimistic and extreme pessimistic representative concentration pathways scenarios RCP 4.5 and RCP 8.5 for the 2050s. Our results show that the distinct distribution patterns of studied Ottelia species were influenced by topography (elevation) and climate (e.g., mean temperature of driest quarter, annual precipitation, and precipitation of the driest month). While there is a lack of accord in defining the limiting factors for the distribution of Ottelia species, it is clear that water-temperature conditions have promising effects when kept within optimal ranges. We also note that climate change will impact Ottelia by accelerating fragmentation and habitat loss. The assessment of niche overlap revealed that Ottelia cylindrica and O. verdickii had slightly more similar niches than the other Ottelia species. The present findings identify the need to enhance conservation efforts to safeguard natural Ottelia populations and provide a theoretical basis for the distribution of various Ottelia species in Africa.
The coastal forests of Kenya are global biodiversity hotspots known for rich plant diversity and endemism. They exist as fragmented forest islands, and their current conservation status and quantitative trends in plant diversity are understudied. We investigated these knowledge gaps by providing a comprehensive literature review and comparing to field data collected using standardized sampling protocol. Our goals were to build a robust basis for future analyses, biodiversity monitoring, and to understand the role of fragment area in determining species richness.
We recorded a total of 937 woody species belonging to 88 families in 30 forest patches from reviewed and sampled data. Species richness per site from literature review was affected by biases in data scarcity, forest size and variation in sampling methods. In general, large forests reserves of Shimba hills and Arabuko exhibited a high number of cumulative species compared to smaller forest patches. Species-area relationship showed a significant proportion of species richness per forest was determined by forest area, according to Arrhenius model.
This study is the first to review forest patch woody plant species diversity knowledge gaps in the coastal forests of Kenya, and the resulting comparison provides the first quantitative overview and foundation of these forests.
Climate and land-use changes are the main drivers of species distribution. On the basis of current and future climate and socioeconomic scenarios, species range projections were made for nine species in the Fabaceae family. Modeled species have instrumental and relational values termed as nature's contribution to people (NCP). For each species, five scenarios were analyzed resulting in 45 species range maps. Representative concentration pathway (RCP) 4.5 and three shared socioeconomic pathways (SSPs 1, 2, and 3) were used in the analysis. Species ranges under these scenarios were modeled using MaxEnt; a niche modeling software that relates species occurrence with environmental variables. Results were used to compute species richness and evenness based on Shannon's diversity Index. Results revealed a mix of range expansion and contraction for the modeled species. The findings highlighted which species may remain competitive in an urbanized future and which ones are detrimentally affected by climate. Parts of the country where species abundances are likely to change due to climate and socioeconomic changes were identified. Management of species will be required in people-dominated landscapes to maintain interactions between nature and society, while avoiding natural resource degradation and loss of NCP.
Ecological niche models (ENMs) are widely used statistical methods to estimate various types of species niches. After lecturing several editions of introductory courses on ENMs and reviewing numerous manuscripts on this subject, we frequently faced some recurrent mistakes: 1) presence-background modelling methods, such as Maxent or ENFA, are used as if they were pseudo-absence methods; 2) spatial autocorrelation is confused with clustering of species records; 3) environmental variables are used with a higher spatial resolution than species records; 4) correlations between variables are not taken into account; 5) machine-learning models are not replicated; 6) topographical variables are calculated from unprojected coordinate systems, and; 7) environmental variables are downscaled by resampling. Some of these mistakes correspond to student misunderstandings and are corrected before publication. However, other errors can be found in published papers. We explain here why these approaches are erroneous and we propose ways to improve them.
Aim
Understanding connectivity between avian breeding and non‐breeding areas is essential to understand processes affecting threatened migrants throughout their annual cycle. We attempted to establish migratory connectivity and flyway structure of the IUCN vulnerable Swan Geese (Anser cygnoides ) by combining citizen science species' distribution models (SDMs) and feather stable isotope analysis.
Locations
Russia, Mongolia and China.
Methods
We established migratory origins and movements of 46 Swan Geese from five wintering locations by integrating citizen science SDMs and feather stable hydrogen isotope (δ²H) measurements by linking feathers to precipitation isoscapes in a Bayesian probability framework.
Results
We determined multiple summering origins among Swan Geese from the most important wintering location, Poyang Lake, Jiangxi Province, China. As predicted, we found no evidence for sex‐biased differences in δ²H measurements. Four geese tracked with GPS/GSM loggers all migrated to moulting areas, which confirmed the accuracy of our predictions from δ²H assignments. Differences between summering ranges inferred from historical and modern samples coincided with major wetland loss in northeastern China since the 1950s. Despite limited historical data, we contend that this supports the hypothesis that the summering range prior to 1950 was much wider than that of the current population.
Main conclusions
This was the first Asian study to establish migratory origins of wintering Anatidae based on stable isotopes and citizen science SDMs. We advocate the wider combined application of SDMs, telemetry studies and stable isotopes to investigate effectively avian migratory connectivity, as the results from this study provided important contributions to the development of conservation measures for this threatened and declining species in East Asia.
The inadequacy of information impedes society’s competence to find out the cause or degree of a problem or even to avoid further losses in an ecosystem. It becomes even harder to identify all the biological resources at risk because there is no exhaustive inventory of either fauna or flora of a particular region. Coastal forests of Kenya are located in the southeast part of Kenya and are distributed mainly in four counties: Kwale, Kilifi, Lamu, and Tana River County. They are a stretch of fragmented forests ca. 30−120 km away from the Indian Ocean, and they have existed for millions of years. Diversity of both fauna and flora is very high in these relicts and the coastal forests of Eastern Africa, extending along the coast from Somalia through Kenya and Tanzania to Mozambique, are ranked among the priority biodiversity hotspot in the world. In spite of the high plant species richness and their importance towards supporting the livelihoods of the communities that live around them, floristic studies in these forests have remained poorly investigated. Hence, based on numerous field investigations, plant lists from published monograph/literature, and data from BRAHMS (Botanical Records and Herbarium Management System) database at East African herbarium (EA), we present a detailed checklist of vascular plants recorded in this region. Our results show that Kenyan coastal forests play an essential role in the flora of Kenya and the plant diversity of the coastal forests of East Africa. The checklist represents 176 families, 981 genera, 2489 species, 100 infraspecific taxa, 90 endemic plants species, 72 exotic species, and 120 species that are included in the current IUCN Red List of Threatened Species as species of major concern. We also discovered three new species to the world from these relicts. Thus, Kenyan coastal forests present a remarkable and significant center of plant diversity.
Plant-disperser relationship is a mutual approach that regulates the species composition and habitat diversity. Here, we unfold the dispersal profile of India and provide comprehensive information on plant-disperser relationships, emphasising on plant longevities (annual, biennial, and perennial), plant life forms (tree, shrub, herb, liana), and vegetation types. The floral data were collected from a national database, and the dispersal information of 3301 geo-tagged plant species was gathered. The plant dispersal types were mainly (1) abiotic (hydrochory-water, anemochory-wind) and (2) biotic (endozoochory-internal gut, epizoochory-adherence to external surface, anthropochory-human, ornithochory-bird, myrmecochory-insect, and chirepterochory-bat) that included five dispersal modes, i.e. monochory (single), dichory (double), trichory (triple), quadrichory (four), and quintuchory (five). The generalised linear model was utilised to evaluate plant-disperser relationships. Monochory could explain variances of 56.8%, 51.2%, and 45.1% in perennials, annuals, and biennials, and 45.3%, 46.3%, 39.4%, and 47.7% for trees, shrubs, herbs, and lianas, respectively. Monochory has more significant influence on all major vegetation types, with at least 40% variance explanation. Anemochory, the dispersal by wind factor, was found to exercise by most plants. The life form wise analytics revealed inclination of multiple modes of dispersal for herbs with abiotic factors might be due to lighter weight, followed by trees with biotic dispersers could be owing to large size seeds. The same trend was reported from herb-dominant grassland where abiotic factors mostly contribute to dispersal, whereas the tree-dominant vegetation types exhibit dispersal primarily due to biotic means. This study provides a synoptic diagnosis to understand the dispersal profile of India, which has been an understudied domain.
We urgently need to predict species responses to climate change to minimize future biodiversity loss and ensure we do not waste limited resources on ineffective conservation strategies. Currently, most predictions of species responses to climate change ignore the potential for evolution. However, evolution can alter species ecological responses, and different aspects of evolution and ecology can interact to produce complex eco‐evolutionary dynamics under climate change. Here we review how evolution could alter ecological responses to climate change on species warm and cool range margins, where evolution could be especially important. We discuss different aspects of evolution in isolation, and then synthesize results to consider how multiple evolutionary processes might interact and affect conservation strategies. On species cool range margins, the evolution of dispersal could increase range expansion rates and allow species to adapt to novel conditions in their new range. However, low genetic variation and genetic drift in small range‐front populations could also slow or halt range expansions. Together, these eco‐evolutionary effects could cause a three‐step, stop‐and‐go expansion pattern for many species. On warm range margins, isolation among populations could maintain high genetic variation that facilitates evolution to novel climates and allows species to persist longer than expected without evolution. This “evolutionary extinction debt” could then prevent other species from shifting their ranges. However, as climate change increases isolation among populations, increasing dispersal mortality could select for decreased dispersal and cause rapid range contractions. Some of these eco‐evolutionary dynamics could explain why many species are not responding to climate change as predicted. We conclude by suggesting that resurveying historical studies that measured trait frequencies, the strength of selection, or heritabilities could be an efficient way to increase our eco‐evolutionary knowledge in climate change biology.
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As climate conditions continue to shift, species assemblages and composition within ecological communities may be reshuffled in unpredictable ways. Some habitat types may cease to exist while others may expand in size; protecting ecosystems and species in their current locations will become increasingly difficult. Threatened and endangered species are likely to be disproportionately affected by climate change because they are often habitat specialists and relatively rare. In the United States, the 1973 Endangered Species Act has prevented the extinction of many species. The identification and conservation of critical habitat is an important tool for species preservation. But how do we designate and preserve habitat for protected species when we are unsure about future habitat conditions? We address this question based upon the concept of managing for anticipated change, rather than focusing on the maintenance of existing conditions. We used an ecological niche modeling program (MaxEnt) to model current and future climatic niche for an endangered mammal endemic to southern California, USA; the Stephens’ kangaroo rat (SKR, Dipodomys stephensi). Our results indicate that the climatic niche of SKR was governed primarily by precipitation during the dry season, precipitation seasonality, annual mean temperature, and mean summer temperature. Projecting current species‐presence relationships to different scenarios predicted for the future revealed substantial loss in climatic niche with increased emission rates. Areas of future suitable climatic niche were evaluated in relation to land ownership and identified as potential reserves or translocation sites, which can aid in conservation planning for this species. Additionally, species vulnerability assessments and a climate‐change analysis tool were utilized to demonstrate how overall understanding of climate change effects can be enhanced. Information presented here can serve as guiding principles for the inclusion of climate change considerations into management plans, and can better inform overall decision‐making related to endangered species management. © 2019 The Wildlife Society. Understanding where climate change impacts may occur, and potential species response, is pivotal for effective conservation planning for threatened and endangered species. We evaluated potential effects of climate change on an endangered mammal in southern California and found that the climatic niche will shrink considerably in the future, with the spatial location, extent and ownership status of suitable habitat areas shifting across the landscape under future climate scenarios.
With a few exceptions, comprehensive lists of alien plants that invade natural ecosystems are lacking in sub-Saharan Africa. Some available lists are either preliminary or localised, or focus on agricultural weeds. This study set out to compile a list of alien plant species that are invading natural ecosystems and rangelands in five countries in eastern Africa, and to map the distribution of the species that threaten ecosystem integrity and productivity. The location of all alien plant species seen during surveys between 2008 and 2016 was recorded using a hand-held GPS device, as well as their status in terms of either being present and/or naturalised, or invasive and spreading. Individual occurrence records were summarised at the scale of half degree grid cells (∼55 km × 55 km). The survey covered almost half (522) of the 1063 grid cells in Ethiopia, Kenya, Tanzania, Uganda and Rwanda. We recorded 164 invasive alien species in 110 genera and 47 families. We provide further information on the distribution and impacts of 30 species considered to have the greatest impacts in terms of transforming natural ecosystems, as well as on a further 21 species with limited distributions that could potentially become ecosystem transformers. Invasive alien plants are clearly a widespread and growing problem in eastern Africa, and capacity to manage them effectively remains a problem. A great deal of work needs to be done to raise awareness of the problem, and to identify appropriate responses that will be effective in resource-poor countries.
Fruit type, an important reproductive trait, is closely related to reproduction strategy, community dynamics and biotic interactions. However, limited research has explored the geographic distribution of fruit type and the underlying abiotic factors influencing this on a large scale. Here we aim to study large-scale distribution patterns of fleshy-fruited plant species and the most important environmental drivers for different growth forms in utilizing the fruit type and distribution data for over 27000 plant species in China. Results indicated that the proportion of fleshy-fruited species was higher in southeast China, and this pattern was consistent between different growth forms. Overall, the proportion of fleshy-fruited species was higher in wet, warm, and stable environments. Notably, mean annual precipitation had the greatest predictive contribution to woody fleshy-fruited species distributions, but mean annual temperature best predicted the herbaceous fleshy-fruited species distributions. We provide the first map of a large-scale distribution of fleshy-fruited plant species for different growth forms in the northern hemisphere and show that these geographic patterns are mainly determined by contrasting climatic gradients. Recognizing that climate factors have different relationships with different growth forms of fleshy-fruited species advances our knowledge about fruit type and environment. This work contributes to predictions of the global distribution of fleshy-fruited species under future climate change scenarios and provides a reference for continued research on the complex interactions between plants, frugivores and the environment.
We discuss here the merits of an explicit resource allocation framework and introduce a prototype decision tool that we developed with the U.S. Fish and Wildlife Service (USFWS) to
facilitate transparent and efficient recover allocation decisions.
Anthropogenic activities caused a severe loss of pristine habitats alongside with fragmentation of remaining habitats and the deterioration of habitat quality. The Arabuko Sokoke forest represents the largest remnant of East African coastal forest. Despite conservation efforts to maintain biodiversity in this forest, populations of several species are declining, including flagship species like the endemic Sokoke Scops Owl Otus ireneae. In this study, we assessed the presence of O. ireneae based on playback technique to identify the occurrence of this species across the forest. We then analysed habitat parameters at sites occupied and non-occupied by the owl species. Our data show that O. ireneae occurs restricted to one single forest type, the Cynometra woodland. Results obtained from parametric one-way ANOVA and structural equation modelling reveal that large old Cynometra webberi trees and dead wood are key requirements for the occurrence of this owl species. However, large C. webberi trees are currently illegally logged by the forest adjacent human population to produce timber, carvings, poles. Charcoal and dead wood represents a major energy source for households. Otus ireneae, being the smallest owl of East Africa may serve as a charismatic flag ship species to promote conservation of this endangered coastal forest, and furthermore as umbrella to conserve other forest species with similar habitat requirements.
The Arabuko Sokoke dryland coastal forest along the East African coastline provides a unique habitat for many endangered endemic animal and plant species. High demographic pressure with subsequent land-splitting, soil depletion in combination with erratic rainfalls and the collapse of the tourism industry are negatively affecting food security and human livelihood quality in this region. Food crops were originally produced by subsistence farming, but have now to be purchased at local- and super-markets, constituting a major financial burden for the local people. In consequence, overexploitation of natural resources from Arabuko Sokoke forest (illegal logging, charcoal burning, poaching of wild animals) increased during the past years. In this commentary we document ecosystem heterogeneity leading to high species richness. We discuss direct and indirect drivers of habitat degradation of the Arabuko Sokoke forest, and critically reflect current and future solutions. Key drivers of habitat destruction and biodiversity loss are (i) illegal timber logging and removal of woody biomass, (ii) poaching of bush-meat, (iii) exceeding of the carrying capacity by the local elephant population, restricted to Arabuko Sokoke by an electric fence, and (iv) weak governance structures and institutional confusion exacerbating illegal exploitation of natural resources. Potential solutions might be: Provisioning of additional income sources; reforestation of the surrounding areas in the framework of REDD+ activities to create a buffer around the remaining primary forest; improving governance structures that formulates clear guidelines on future usage and protection of natural resources within the Arabuko Sokoke forest; and family planning to counteract human demographic pressure and the exploitation of natural resources.
The Arabuko Sokoke dryland coastal forest along the East African coastline provides a unique habitat for many endangered endemic animal and plant species. High demographic pressure with subsequent land-splitting, soil depletion in combination with erratic rainfalls and the collapse of the tourism industry are negatively affecting food security and human livelihood quality in this region. Food crops were originally produced by subsistence farming, but have now to be purchased at local- and super-markets, constituting a major financial burden for the local people. In consequence, overexploitation of natural resources from Arabuko Sokoke forest (illegal logging, charcoal burning, poaching of wild animals) increased during the past years. In this commentary we document ecosystem heterogeneity leading to high species richness. We discuss direct and indirect drivers of habitat degradation of the Arabuko Sokoke forest, and critically reflect current and future solutions. Key drivers of habitat destruction and biodiversity loss are (i) illegal timber logging and removal of woody biomass, (ii) poaching of bush-meat, (iii) exceeding of the carrying capacity by the local elephant population, restricted to Arabuko Sokoke by an electric fence, and (iv) weak governance structures and institutional confusion exacerbating illegal exploitation of natural resources. Potential solutions might be: Provisioning of additional income sources; reforestation of the surrounding areas in the framework of REDD+ activities to create a buffer around the remaining primary forest; improving governance structures that formulates clear guidelines on future usage and protection of natural resources within the Arabuko Sokoke forest; and family planning to counteract human demographic pressure and the exploitation of natural resources.
We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km 2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 ∘ C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.
Species distribution models (SDMs) are widely used to predict the occurrence of species. Because SDMs generally use presence-only data, validation of the predicted distribution and assessing model accuracy is challenging. Model performance depends on both sample size and species’ prevalence, being the fraction of the study area occupied by the species. Here, we present a novel method using simulated species to identify the minimum number of records required to generate accurate SDMs for taxa of different pre-defined prevalence classes. We quantified model performance as a function of sample size and prevalence and found model performance to increase with increasing sample size under constant prevalence, and to decrease with increasing prevalence under constant sample size. The area under the curve (AUC) is commonly used as a measure of model performance. However, when applied to presence-only data it is prevalence-dependent and hence not an accurate performance index. Testing the AUC of an SDM for significant deviation from random performance provides a good alternative. We assessed the minimum number of records required to obtain good model performance for species of different prevalence classes in a virtual study area and in a real African study area. The lower limit depends on the species’ prevalence with absolute minimum sample sizes as low as 3 for narrow-ranged and 13 for widespread species for our virtual study area which represents an ideal, balanced, orthogonal world. The lower limit of 3, however, is flawed by statistical artefacts related to modelling species with a prevalence below 0.1. In our African study area lower limits are higher, ranging from 14 for narrow-ranged to 25 for widespread species. We advocate identifying the minimum sample size for any species distribution modelling by applying the novel method presented here, which is applicable to any taxonomic clade or group, study area or climate scenario.
Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.
Economically, legumes (Fabaceae) represent the second most important family of crop plants after the grass family, Poaceae. Grain legumes account for 27% of world crop production and provide 33% of the dietary protein consumed by humans, while pasture and forage legumes provide vital part of animal feed. Fabaceae, the third largest family of flowering plants, has traditionally been divided into the following three subfamilies: Caesalpinioideae, Mimosoideae, and Papilionoideae, all together with 800 genera and 20,000 species. The latter subfamily contains most of the major cultivated food and feed crops. Among the grain legumes are some of mankind's earliest crop plants, whose domestication parallelled that of cereals: Soybean in China; faba bean, lentil, chickpea and pea in the Fertile Crescent of the Near East; cowpeas and bambara groundnut in Africa; soybean and mungbeans in East Asia; pigeonpea and the grams in South Asia; and common bean, lima bean, scarlet runner bean, tepary bean and lupin in Central and South America. The importance of legumes is evidenced by their high representation in ex situ germplasm collections, with more than 1,000,000 accessions worldwide. A detailed knowledge of the phylogenetic relationships of the Fabaceae is essential for understanding the origin and diversification of this economically and ecologically important family of angiosperms. This review aims to combine the phylogenetic and genetic diversity approaches to better illustrate the origin, domestication history and preserved germplasm of major legume crops from 13 genera of six tribes and to indicate further potential both for science and agriculture.
Aim
Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence‐only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence‐only data.
Location
We used real spatially explicit environmental data derived from the western part of the state of V ictoria, south‐eastern A ustralia, and simulated species distributions within this area.
Methods
Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence‐only data. We further adopted a simulation approach, created many virtual species with differing prevalences in a real landscape in south‐eastern A ustralia, generated data sets with different proportions of pseudo‐absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations.
Results
Three threshold selection methods were not affected by pseudo‐absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence‐only data to those determined from presence/absence data.
Main conclusions
Max SSS is a promising method for threshold selection when only presence data are available.
Many studies of postdispersal seed fate use seed removal as an index of seed predation. However, following primary seed dispersal, some seeds are transported intact by ants, dung beetles, scatter-hoarding animals, or abiotic processes to new microsites (secondary dispersal) where germination is possible. Despite a growing realization that secondary seed dispersal can play an important role in plant recruitment, many researchers continue to use seed removal as a proxy for seed predation and are focused too intently on only the initial step of seed fate. We describe, using examples from the recent literature, how the results of some seed removal studies may have been misinterpreted, present plausible, alternative explanations for the fate of seeds in those studies, and discuss the importance of detailed studies of seed fates. Following the fates of seeds can be difficult, but such studies contribute much more to our understanding of seed dynamics and plant fitness.
Background and aims - Plants are often overlooked in conservation planning, yet they are the foundation of all terrestrial ecosystems. The East Africa region is used to investigate the effectiveness of protected areas for conserving plants. With a wide range of ecosystems and 771 protected areas covering nearly one quarter of the land area, East Africa is an ideal location to assess the effectiveness of protected areas through distribution modelling of the genus Acacia.
Methods - Herbarium specimen data (2,047 records) were collated from East Africa for 65 taxa (species, subspecies, varieties) from the genus Acacia. Generalised Additive Models were used to determine climatic drivers, and thence to extrapolate climatic suitability across the region. For two Acacia taxa, we investigated the potential for climate-induced range-shifts using a downscaled regional climate model under two IPCC scenarios.
Key results - Approximately two thirds of Acacia diversity hotspots had < 10% coverage by protected areas. Furthermore, the protected area network covered less of the predicted ranges of the Acacia taxa and contained fewer taxa per unit area than would be expected under randomised placement. Areas with suitable climate for high-elevation, moisture-dependent taxa such as A. abyssinica subsp. calophylla are predicted to contract their potential range by up to 80% towards mountain peaks, where protected areas are dominated by low-level protection forest reserves. Conversely, the area of suitable environment for a xerophytic low-elevation species (A. turnbulliana) is predicted to increase by up to 77%.
Conclusions - East Africa's national parks may not be preserving an important component of ecosystem diversity, a situation exacerbated by climate change. Even within the genus Acacia, different species are predicted to respond differently to climate change. Priority areas for research and conservation are identified based on overlap between predicted high Acacia diversity and gaps in the collection record, with northern and eastern Kenya highlighted as particularly important. High elevation protected areas are also predicted to become increasingly important as climatic refugia in a warmer future.
Introduces Global 200, a representation of habitat types on a global scale for environmental conservation. Stratification of ecoregions by realms; Boundaries of terrestrial ecoregions; Variation of ecoregions according to biological distinctiveness; Terrestrial ecoregion boundaries.
Together with the Coastal Forests and Eastern Arc rainshadow, the Eastern Arc forests make up a botanical Centre of Endemism in Eastern Tropical Africa (CEETA), which covers a wide range of vegetation formations in four different phytochoria. The factors that gave rise to the concentration of restricted-range taxa in the different vegetation types appear to result from the same long-term geological and climatic processes. The endemic-rich vegetation types occur in three countries: Mozambique, Tanzania and Kenya and are managed under a wide range of land tenure arrangements from public land and private ownership, to Forest Reserve, Game Reserve and National Park. Much of the CEETA is recognised as a biodiversity ‘hotspot’ of global importance, but lacks a common management strategy. A possible common framework within which to develop an appropriate strategy is that of the World Heritage Convention. The case of the Australian Wet Tropics World Heritage Site is discussed as a comparative example.
Umbrella species are ‘species with large area requirements, which if given sufficient protected habitat area, will bring many other species under protection’. Historically, umbrella species were employed to delineate specific reserve boundaries but are now used in two senses: (1) as aids to identifying areas of species richness at a large geographic scale; (2) as a means of encompassing populations of co-occuring species at a local scale. In the second sense, there is a dilemma as to whether to maximize the number or viability of background populations; the umbrella population itself needs to be viable as well. Determining population viability is sufficiently onerous that it could damage the use of umbrella species as a conservation shortcut. The effectiveness of using the umbrella-species concept at a local scale was investigated in the real world by examining reserves in East Africa that were gazetted some 50 years ago using large mammals as umbrella species. Populations of these species are still numerous in most protected areas although a few have declined. Populations of other, background species have in general been well protected inside reserves; for those populations that have declined, the causes are unlikely to have been averted if reserves had been set up using other conservation tools. Outside one reserve, Katavi National Park in Tanzania, background populations of edible ungulates and small carnivores are lower than inside the reserve but small rodent and insectivore abundance is higher. While we cannot compare East African reserves to others not gazetted using umbrella species, the historical record in this region suggests that umbrella species have been an effective conservation shortcut perhaps because most reserves were initially large and could encompass substantial populations of background species. It is therefore premature to discard the local-scale umbrella-species concept despite its conceptual difficulties.
Global changes, from habitat loss and invasive species to anthropogenic climate change, have initiated the sixth great mass
extinction event in Earth’s history. As species become threatened and vanish, so too do the broader ecosystems and myriad
benefits to human well-being that depend upon biodiversity. Bringing an end to global biodiversity loss requires that limited
available resources be guided to those regions that need it most. The biodiversity hotspots do this based on the conservation
planning principles of irreplaceability and vulnerability. Here, we review the development of the hotspots over the past two
decades and present an analysis of their biodiversity, updated to the current set of 35 regions. We then discuss past and
future efforts needed to conserve them, sustaining their fundamental role both as the home of a substantial fraction of global
biodiversity and as the ultimate source of many ecosystem services upon which humanity depends.
The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1 degrees results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4 degrees-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Nino-Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden-Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4 degrees C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.
To increase the understanding of the ethnopharmacology of a single species, elaboration of dispersed primary data is required. Tamarindus indica L. (Fabaceae), or tamarind, is a common tree, especially in West Africa, with a good potential to contribute to affordable local health care based on traditional medicine (TM). For this single species review, more than 60 references with detailed information on the ethnopharmacology of Tamarindus indica in the African context were selected. It showed that most prominently, the fruits are used as laxative or febrifuge throughout the Sahel and Soudan ecological zones. Tamarind bark and leaves are often involved in the treatment of wounds, especially in central West Africa. While the bark is used to treat diarrhoea in West Africa, the leaves are used for this purpose in East Africa. Our findings suggest a difference in the way tamarind is used between East and West Africa and we assess the similarities of its uses within those regions. This review demonstrates the capability of literature research to reveal knowledge by mining and compiling information from the growing body of primary ethnopharmacologic data, much of which is published in this journal. By creating a specific profile of tamarind in the context of traditional medicine throughout Africa, the authors contribute to the collection of current ethnobotanic species accounts on Tamarindus indica that tend to be qualitative and more general.
A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
One of the anthropogenic causes affecting species distribution is climate change, which has significant implications for species conservation. However, little is known about the effects of changes in parasitic plant distribution on community-level interactions. Parasitic flowering plants make a limited numerical contribution to biodiversity. Their lifestyle may exhibit a moderate to the high degree of host dependence. Because of this host dependence, parasites may be more affected by environmental changes, such as climate change, compared to autotrophic representatives. To our knowledge, the effects of different climate change scenarios and their environmental variables on parasitic plants and their hosts have not yet been studied. This study aimed to construct a model which shows the current and future potential effects of climate change on the distribution of the two holoparasitic plants Hydnora abyssinica A.Br., and H. africana Thunb. in comparison to their respective Fabaceae and Euphorbiaceae hosts. We projected the future distribution of these species and their host plants using five models, nine bioclimatic, and five environmental variables. The global circulation model (CMIP5) for the years 2050 and 2070, applying two representative concentration pathways scenarios (RCP4.5 and RCP8.5) projected a 41–64% contraction of suitable habitats for H. abyssinica. For H. africana, more stable conditions are estimated, with a 12–28% contraction in suitable habitats, making this species putatively less prone to climate change effects, although this species has a more restricted distribution compared to H. abyssinica. Because climate change could affect the host differently than the parasites, the impact on the parasite could potentially be exacerbated due to host plant dependence. The models predict that the host plant distribution will be less affected, except for Vachelia Karroo, Vachellia xanthophloea, and Euphorbia gregaria, which indicated high contraction (40–66%). The predicted host species distribution ranges will only partially overlap with the respective distribution of the parasite.
Global climate change is gradually altering species distribution and spatial patterns of diversity. Yet, climatic factors influencing the local distribution and habitat preferences for southern African species remain largely unexplored. As such, predicting species distribution patterns and identifying environmental covariates that promote species range expansion will be critical in developing management protocols for biodiversity protection. Maxent, a species distribution model algorithm that applies a maximum entropy machine learning technique, is used in this study to map the geographical distributions of appropriate habitats for Colophospermum mopane (J.Kirk ex Benth.) J. Léonard in southern Africa under current and future climate change scenarios. We identified the highest contributors to the modelled distributions and calculated the range changes (expansion or loss) in southern Africa for C. mopane under three Representative concentration pathways (RCPs) for the 2050s and 2070s. Our results showed that the distribution of C. mopane was mainly influenced by solar radiation, annual temperature range, and annual precipitation. We also observe that C. mopane is distributed continuously in southern Africa, from southern Angola and northern Namibia to central-southern Mozambique, with a total occurrence area of 1,281,242 km2. According to the species response curves, this species preferred habitats with annual precipitation of 130-200 mm, an annual temperature range of 22 - 28˚ C, and elevations of about 500-1000 m above sea level. Under climate change scenarios, suitable habitat areas reduced significantly in the northern limits of the potential distribution areas while they expanded in the southern limits. Overall, the appropriate habitat areas will likely expand the least (4.08 - 4.46%) and decline the most (8.83 - 10.08%) under the extreme scenario of RCP8.5, depending on the year. Although there is a lack of consensus on the range changes in future distributions among the various RCPs, it is clear that solar radiation will significantly limit the distribution of C. mopane. This knowledge is important for landscape planners and rangeland managers working to safeguard biodiversity from extinction.
Mapping and modeling suitable habitat and distribution of aquatic species is important to help assess the impact of factors such as climate change in affecting the shift, decline, or expansion of species habitat ranges. In Africa, the distribution of water lily (Nymphaea) species is geographically varied and the habitats suitable for individual species are prone to effects of global warming, though only limited conservation measures have been taken to date in aquatic environments. In this study, four widely distributed water lily species (N. nouchali, N. micrantha, N. lotus, and N. heudelotii) were modeled using MaxEnt which highlighted the individual species’ suitable climatic distribution. The current distribution indicates a partial distribution of N. nouchali in West Africa unlike N. micrantha, N. lotus, and, N. heudelotii. Nymphaea lotus displays wider distribution in West, East, and parts of South African countries including their coastlines compared to all other species. Nymphaea nouchali indicates dense distribution in countries South of Africa while N. micrantha and N. heudelotii in West African countries. Greater habitat changes were noticed between the future and the past projection due to limited range expansion in 2.6, 4.5, and 8.5 (2050) Representative Concentration Pathways (RCPs) in almost all species. The species’ suitable habitat distribution was mainly influenced by nine variables, mostly the temperature and precipitation variables. This study provides projections of future climatic scenarios potentially influencing the distribution of Nymphaea species in Africa, which may be useful for the ongoing conservation and management of these plants especially in areas loosing suitable climatic conditions.
Chapter 2 deals with the bioclimatic classification of Africa within its main bioclimatic divisions, as defined in Chapter 1: Mediterranean and Subtropical to Tropical, and Equatorial. The Sahara is considered apart, as it experiences a Mediterranean bioclimate to the north and a tropical bioclimate to the south. The other African deserts are considered within their general families (Tropical and Equatorial). Fourteen large tables show the various bioclimates with their corresponding climatic, agronomic and biological characteristics. Two detailed tables show the distribution of some 200 key plant species in the various bioclimatic entities identified, between the equator and the Tropic of Cancer. A similar table shows the distribution of some 170 species of mammals in the same zone. Another table shows the distribution of crop species as a function of elevation in eastern Africa. An extensive table shows the surface areas of the main bioclimatic zones identified. Yet another table shows the main bioclimatic requirements of 53 African crops. A colour figure shows the ordination of the climatic, biologic and agronomic parameters in the bioclimatic zoning for the complex case of Kenya.
Species distribution models ( SDM s) are broadly used in ecological and evolutionary studies. Almost all SDM methods require extensive data preparation in a geographic information system ( GIS ) prior to model building. Often, this step is cumbersome and, if not properly done, can lead to poorly parameterized models or in some cases, if too difficult, prevents the realization of SDM s. Further, for many studies, the creation of SDM s is not the final result and the post‐modelling processing can be equally arduous as other steps.
SDM toolbox is designed to facilitate many complicated pre‐ and post‐processing steps commonly required for species distribution modelling and other geospatial analyses. SDM toolbox consists of 59 P ython script‐based GIS tools developed and compiled into a single interface.
A large set of the tools were created to complement SDM s generated in M axent or to improve the predictive performance of SDM s created in M axent. However, SDM toolbox is not limited to analyses of M axent models, and many tools are also available for additional analyses or general geospatial processing: for example, assessing landscape connectivity of haplotype networks (using least‐cost corridors or least‐cost paths); correcting SDM over‐prediction; quantifying distributional changes between current and future SDM s; or for calculating several biodiversity metrics, such as corrected weighted endemism.
SDM toolbox is a free comprehensive python‐based toolbox for macroecology, landscape genetic and evolutionary studies to be used in Arc GIS 10.1 (or higher) with the S patial A nalyst extension. The toolkit simplifies many GIS analyses required for species distribution modelling and other analyses, alleviating the need for repetitive and time‐consuming climate data pre‐processing and post‐ SDM analyses.
The variation of near-surface air temperature anomalies in Africa between 1979 and 2010 is investigated primarily using Microwave Sounding Unit (MSU) total lower-tropospheric temperature data from the Remote Sensing Systems (RSS) and the University of Alabama in Huntsville (UAH) datasets. Significant increasing temperature trends were found in each of the following regions examined: all of Africa, Northern Hemisphere Africa, Southern Hemisphere Africa, tropical Africa, and subtropical Africa. Considering the months June-August, regions in both North and South Africa saw significantly warmer temperatures in the most recent period 1995-2010 than in the period 1979-94. However, for the months December-February, the significant warming was concentrated in the north of Africa. When the two most recent decades are compared with the period 1979-90, warming is observed over these same regions and is concentrated in the most recent decade, from 2001 to 2010. The results presented here indicate that the climate change over Africa is likely not predominantly a result of variations in the El Nino-Southern Oscillation (a teleconnection that has been previously shown to affect climate in some parts of Africa). Instead the climate changes likely occur owing to other natural variability of the climate and/or may be a result of human activity. However, even without ascertaining the specific causes, the most important finding in this work is to demonstrate that a significant rise in African temperatures occurred between 1979 and 2010.
Consumer preferences and scientific developments are changing and this is leading to a significant ad-justment for US agriculture. During the last century, most agronomic research and production were to in-crease yields of food and fiber (Abelson 1994). However, during the last decade more attention is being focused on the production of new and alternative crops and their by-products for industrial, and pharmaceuti-cal use. The legume family (Fabaceae) is the third largest family of flowering plants, with approximately 650 genera and nearly 20,000 species (Doyle 1994). Its species range from large tropical canopy trees to small herbs found in temperate zones, humid tropics, arid zones, highlands, savannas, and lowlands (NPGS 1995). The Fabaceae contains many taxa of industrial, or pharmaceutical importance. Legume seeds are the second most important plant source of human and animal food (Vietmeyer 1986). Other new products would include new food sources, but the majority would provide industrial products such as dyes from Indigo, fiber pulps, vegetable, and pharmaceutical products. Many legumes contain organic chemicals in sufficient quan-tity to be economically useful as feedstocks or raw materials for many scientific, technological, and commer-cial applications. Legumes can biologically fix nitrogen, adding annually up to 500 kg N/ha/year to the soil (NAS/NRC 1979). Not only do other legume species provide hope for combating food shortages in develop-ing countries, but they also can provide many specialty products such as rotenoids (Balandrin et al. 1985) for use as pesticides in developed countries. Genetic variation in legume species and their wild relatives is of prime importance to the successful breeding of improved crop cultivars with added value and durable resistance to pests. The collection and preservation of legume germplasm has been established to ensure that scientists have access to as many genes as possible. The USDA, ARS Plant Genetic Resources Conservation Unit (PGRCU) is dedicated to acquir-ing, conserving, characterizing, evaluating, documenting, and distributing the genetic resources of crops, in-cluding special-purpose legumes. More than 4,000 accessions of special-purpose legumes are stored as seed at-18掳C at the USDA, ARS, PGRCU in Griffin, Georgia. The purpose of this article is to highlight some outstanding new uses where some underexploited legumes seem notably promising.
An annotated checklist of the Shimba Hills in Kwale District is presented. The checklist includes the plants found in the Shimba Hills National Reserve, Mkongani North and West Forest Reserves, Matuga, Mwaluganje Forest Reserve and Elephant Sanctuary, as well as Kaya Chombo, Kaya Teleza, Chitsanze Sacred Grove and the recently destroyed Kaya Miyani. One thousand three hundred and ninety six (1396) plant taxa in 145 families and 686 genera are documented. This represents 44% of the coastal flora and 21% of the Kenyan flora. For each taxon recorded, I also present recent synonyms, a reference specimen, a more precise locality within the checklist area, a short description of its habit and a diagnostic characteristic, as well as some notes on its distribution and conservation status.
Aim
This paper has as its central aim the location of centres of species richness and endemism in the sub‐Saharan African flora. Previous postulation of these centres has been based on an intuitive interpretation of distributional data; this paper provides a test of these centres. A second aim is to establish whether the two indices, richness and endemism, locate the same centres. Thirdly the relationship between species richness and endemism, and latitude and rainfall are explored.
Location
The study area includes much of sub‐Saharan Africa, but excludes the species‐poor southern margin of the Sahara and the Namib–Kalahari regions.
Methods
Analyses were based on 1818 species, scored on a 2.5 × 2.5 degree grid. Species richness was inferred from a simple grid‐diversity count; endemism was determined by three measures: the number of species restricted to two grids, the sum of the inverse of the ranges of the component species of each grid, and the proportion of the species in each grid that have restricted ranges.
Results
The African flora shows a remarkably profound patterning, both in species richness and endemism. The two measures locate largely the same centres, although the rank order among them differs. These centres are: the Cape Floristic Region, East Coast, Congo‐Zambezi watershed, Kivu, Upper and Lower Guinea. Richness is strongly related to maximum rainfall, but there are no obvious correlations between modern climate and endemism. Species richness and endemism north of the equator is much more concentrated into centres than south of the equator.
Main conclusions
There are strongly developed refugia in sub‐Saharan Africa. North of the equator, these refugia are sharply delimited and rather small, separated by large areas of very low endemism. South of the equator endemism tends to be more generally distributed. Variation in species richness in sub‐Saharan Africa can be explained largely by modern rainfall, while endemism may be related to palaeoclimatic fluctuations. Both species richness and endemism show a strong skewing towards the south, indicating that the fluctuations in the Sahara might have influenced the modern distribution of plants in Africa.
In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion.
In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel.
The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence.
When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence.
Synthesis and applications . Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.
Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO2 conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.
MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south-west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.
We compared predictive success in two common algorithms for modeling species’ ecological niches, GARP and Maxent, in a situation that challenged the algorithms to be general – that is, to be able to predict the species’ distributions in broad unsampled regions, here termed transferability. The results were strikingly different between the two algorithms – Maxent models reconstructed the overall distributions of the species at low thresholds, but higher predictive levels of Maxent predictions reflected overfitting to the input data; GARP models, on the other hand, succeeded in anticipating most of the species’ distributional potential, at the cost of increased (apparent, at least) commission error. Receiver operating characteristic (ROC) tests were weak in discerning models able to predict into broad unsampled areas from those that were not. Such transferability is clearly a novel challenge for modeling algorithms, and requires different qualities than does predicting within densely sampled landscapes – in this case, Maxent was transferable only at very low thresholds, and biases and gaps in input data may frequently affect results based on higher Maxent thresholds, requiring careful interpretation of model results.
Ecology Letters (2012) 15 : 365–377
Abstract
Many studies in recent years have investigated the effects of climate change on the future of biodiversity. In this review, we first examine the different possible effects of climate change that can operate at individual, population, species, community, ecosystem and biome scales, notably showing that species can respond to climate change challenges by shifting their climatic niche along three non‐exclusive axes: time (e.g. phenology), space (e.g. range) and self (e.g. physiology). Then, we present the principal specificities and caveats of the most common approaches used to estimate future biodiversity at global and sub‐continental scales and we synthesise their results. Finally, we highlight several challenges for future research both in theoretical and applied realms. Overall, our review shows that current estimates are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered. Yet, the majority of models indicate alarming consequences for biodiversity, with the worst‐case scenarios leading to extinction rates that would qualify as the sixth mass extinction in the history of the earth.
Studies suggest that populations of different species do not decline equally after habitat loss. However, empirical tests have been confined to fine spatiotemporal scales and have rarely included plants. Using data from 89,365 forest survey plots covering peninsular Spain, we explored, for each of 34 common tree species, the relationship between probability of occurrence and the local cover of remaining forest. Twenty-four species showed a significant negative response to forest loss, so that decreased forest cover had a negative effect on tree diversity, but the responses of individual species were highly variable. Animal-dispersed species were less vulnerable to forest loss, with six showing positive responses to decreased forest cover. The results imply that plant-animal interactions help prevent the collapse of forest communities that suffer habitat destruction.
Biogeographic patterns of the East African coastal forest vertebrate fauna
- E T Azeria
- I Sanmartín
- S Carlson
- A Burgess
Azeria, E.T., Sanmartín, I., A
s, S., Carlson, A., Burgess, N., 2007. Biogeographic patterns of
the East African coastal forest vertebrate fauna. Biodiv. Conser. 16 (4), 883-912.
Twenty-five economically important plant families
- Bennett
Bennett, B., 2011. Twenty-five economically important plant families. Encyclopedia of
Life Support Systems. Economic Botany.
Bioclimatic Classification. Bioclimatology and Biogeography of Africa
- Le Hou Erou
Le Hou erou, H.N., 2009. Bioclimatic Classification. Bioclimatology and Biogeography of
Africa. Springer, Berlin, Heidelberg, pp. 79-124. https://doi.org/10.1007/978-3-540-85192-9_2.
Threats to medicinal plant species-an African perspective, conserving medicinal species: Securing a Healthy Future. International Union for Conservation of Nature and Natural Resources
- P Maundu
- P Kariuki
- O Eyog-Matig
Maundu, P., Kariuki, P., Eyog-Matig, O., 2006. Threats to medicinal plant species-an
African perspective, conserving medicinal species: Securing a Healthy Future.
International Union for Conservation of Nature and Natural Resources. Ecosyst.
Livelihoods Group 47-63 2006.