Habitat specialization predicts genetic response to fragmentation in tropical birds

Habitat fragmentation is one of the most severe threats to biodiversity as it may lead to changes in population genetic structure, with ultimate modifications of species evolutionary potential and local extinctions. Nonetheless, fragmentation does not equally affect all species and identifying which ecological traits are related to species sensitivity to habitat fragmentation could help prioritization of conservation efforts. Despite the theoretical link between species ecology and extinction proneness, comparative studies explicitly testing the hypothesis that particular ecological traits underlies species‐specific population structure are rare. Here, we used a comparative approach on eight bird species, co‐occurring across the same fragmented landscape. For each species, we quantified relative levels of forest specialization and genetic differentiation among populations. To test the link between forest specialization and susceptibility to forest fragmentation, we assessed species responses to fragmentation by comparing levels of genetic differentiation between continuous and fragmented forest landscapes. Our results revealed a significant and substantial population structure at a very small spatial scale for mobile organisms such as birds. More importantly, we found that specialist species are more affected by forest fragmentation than generalist ones. Finally, our results suggest that even a simple habitat specialization index can be a satisfying predictor of genetic and demographic consequences of habitat fragmentation, providing a reliable practical and quantitative tool for conservation biology.     

Genetic consequences of local population extinction and recolonization

Theoretical results have shown that a pattern of local extinction and recolonization can have significant consequences for the genetic structure of subdivided populations; consequences that are relevant to issues in both evolutionary and conservation biology. The nature of those consequences depends largely on the mode of colony formation. Extinction and recolonization can either increase or decrease the genetic differentiation of local populations and can lead to a loss of the genetic diversity stored in an array of populations. Recent ecological studies of two insect species have revealed population structures resembling, in part, that considered in the models. They serve to illustrate the potential complexity of the processes of extinction and recolonizatiion in nature.     

Development of a software tool for in silico simulation of Escherichia coli using a visual programming environment

This study describes the development of a software tool, EcoSim, to assist users in implementing quantitative in silico simulation easily. It consists of four parts: extracellular environment and constraints setting mode, table for optimal metabolic flux distribution and chart for changes of substrate concentration, dynamic flux distribution viewer and dynamic hierarchical regulatory network viewer. Representation of a hierarchical regulatory network was constructed with defined modeling symbols and weight in the central Escherichia coli metabolism. All programming procedures for EcoSim were accomplished in a visual programming environment (LabVIEW). To illustrate quantitative in silico simulation with EcoSim, this program was performed on E. coli using glucose and acetate as carbon sources. The simulation results were in agreement with the experimental data obtained from the literature. EcoSim can be used to assist biologists and engineers in predicting and interpreting dynamic behaviors of E. coli under a variety of environmental conditions.     

Giant pandas are losing their edge: Population trend and distribution dynamic drivers of the giant panda

Comprehending the population trend and understanding the distribution range dynamics of species are necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species' environmental preferences and formulating protection policies. Here, we studied the rear-edge population of the flagship species, giant pandas (Ailuropoda melanoleuca), to (1) assess their population trend using their distribution patterns, (2) evaluate their distribution dynamics change from the second (1988) to the third (2001) survey (2–3 Interval) and third to the fourth (2013) survey (3–4 Interval) using a machine learning algorithm (eXtremely Gradient Boosting), and (3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the second survey (k = 1.050), improved by the third survey (k = 0.97), but deteriorated by the fourth survey (k = 0.996), which indicates a worrying population future. We found that precipitation had the most significant influence on distribution dynamics among several potential environmental factors, showing a negative correlation between precipitation and giant panda expansion. We recommend that further research is needed to understand the microenvironment and animal distribution dynamics. We provide a fresh perspective on the dynamics of giant panda distribution, highlighting novel focal points for ecological research on this species. Our study offers theoretical underpinnings that could inform the formulation of more effective conservation policies. Also, we emphasize the uniqueness and importance of the Liangshan Mountains giant pandas as the rear-edge population, which is at a high risk of population extinction.     

Predicting potential distributions of two rare allopatric sister species,the globally threatened Doliornis cotingas in the Andes

ABSTRACT Conservation of rare, elusive species is difficult because of limited knowledge of their biology and distribution. The two species of Doliornis cotingas are known from 7 (Chestnut‐bellied Cotinga, D. remseni) and 13 (Bay‐vented Cotinga, Doliornis sclateri) locations, respectively. Their limited ranges in combination with habitat loss make them vulnerable to extinction. We modeled the potential distribution of these two rare, allopatric sister species separated by an orographic barrier using species distribution modeling with an ensemble forecast approach using eight modeling techniques. Predicted distributions (with conservative thresholds of probability of presence, i.e., lowest presence thresholds) for these sister species showed virtually no overlap, although their respective niches were statistically not dissimilar. Hence, the existence of the recently discovered Chestnut‐bellied Cotinga could not have been predicted from the range of its sister species, unless using very crude distribution models (with extensive extrapolation). New areas of likely occurrences were identified for both species, and the genus, and will be useful for directing future field searches. The estimates of potentially suitable range for both species still qualify both species as “vulnerable to extinction.” Our study illustrates how opportunistic records collected by field ornithologists can be objectively transformed, with the help of existing software, into information potentially useful in the conservation of rare species.     

Drivers of extinction risk in African mammals: the interplay of distribution state,human pressure,conservation response and species biology

Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.     

Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.     

种群生存力分析研究进展和趋势

种群生存力分析（PVA）是正在迅速发展的新方法,已成为保护生物学研究的热点。它主要研究随机干扰对小种群绝灭的影响,其目的是制定最小可存活种群（MVP）,把绝灭减少到可接受的水平。随机干扰可分四类;统计随机性,环境随机性,自然灾害和遗传随机性。确定MVP的方法有三种：理论模型,模拟模型,模拟模型和岛屿生物地理学方法。理论模型主要研究理想或特定条件下随机因素对种群的影响;模拟模型是利用计算机模拟种群绝灭过程;岛屿生物地理学方法主要分析岛屿物种的分布和存活,证实分析模型和模拟模型。已有大量的文献研究统计随机性,环境随机性和自然灾害的行为特征,但遗传因素与种群生存力之间的关系还不清楚。建立包括四种随机性的综合性模型,广泛地检验PVA模型,系统地研制目标种的遗传和生态特性以及MVP的实际应用是PVA的发展趋势。     

Applications and techniques for non-invasive faecal genetics research in felid conservation

Non-invasive genetic techniques utilising DNA extracted from faeces hold great promise for felid conservation research. These methods can be used to establish species distributions, model habitat requirements, analyse diet, estimate abundance and population density, and form the basis for population, landscape and conservation genetic analyses. Due to the elusive nature of most felid species, non-invasive genetic methods have the potential to provide valuable data that cannot be obtained with traditional observational or capture techniques. Thus, these methods are particularly valuable for research and conservation of endangered felid species. Here, we review recent studies that use non-invasive faecal genetic techniques to survey or study wild felids; provide an overview of field, laboratory and analysis techniques; and offer suggestions on how future non-invasive genetic studies can be expanded or improved to more effectively support conservation.     

Testing the consistency of connectivity patterns for a widely dispersing marine species

Connectivity is widely recognized as an important component in developing effective management and conservation strategies. Although managers are generally most interested in demographic, rather than genetic connectivity, new analytic approaches are able to provide estimates of both demographic and genetic connectivity measures from genetic data. Combining such genetic data with mathematical models represents a powerful approach for accurately determining patterns of population connectivity. Here, we use microsatellite markers to investigate the genetic population structure of the New Zealand Rock Lobster, Jasus edwardsii, which has one of the longest known larval durations of all marine species (>2 years), a very large geographic range (>5500 km), and has been the subject of extensive dispersal modeling. Despite earlier mitochondrial DNA studies finding homogeneous genetic structure, the mathematical model suggests that there are source-sink dynamics for this species. We found evidence of genetic structure in J. edwardsii populations with three distinct genetic groups across New Zealand and a further Australian group; these groups and patterns of gene flow were generally congruent with the earlier mathematical model. Of particular interest was the consistent identification of a self-recruiting population/region from both modeling and genetic approaches. Although there is the potential for selection and harvesting to influence the patterns we observed, we believe oceanographic processes are most likely responsible for the genetic structure observed in J. edwardsii. Our results, using a species at the extreme end of the dispersal spectrum, demonstrate that source-sink population dynamics may still exist for such species.     

中国濒危兽类保护遗传学研究进展与展望

我国是全球生物多样性大国,拥有包括大熊猫、金丝猴、华南虎、麋鹿、白鱀豚等特有物种和旗舰物种在内的丰富兽类资源。近几十年来,土地利用模式转变、盗猎、环境污染、气候变化等因素使许多兽类物种面临生存威胁,导致物种遗传多样性丧失。而遗传多样性是生物多样性的基本组成部分,决定了物种和种群能否长期生存。保护遗传学作为保护生物学的一大分支学科,旨在通过遗传学分析探明种群遗传变异和物种濒危的遗传学机制。近40年来,随着研究手段和技术的不断发展,我国兽类保护遗传学在遗传多样性和近交水平评估、景观遗传学、生态遗传学和圈养种群遗传管理等方面都取得了重要成果。然而,未来人类社会发展可能为濒危兽类带来的威胁依然存在,高通量测序等新技术的进一步发展则能够帮助我们更加深入地了解濒危物种和种群遗传适应与濒危机制,从而实现对濒危兽类的有效管理与保护。     

植物遗传资源的种子基因库保存

一个物种的灭绝是与其受生物因子和非生物因子的威胁程度相关的。随着物种的加速绝灭,保护生物多样性受到广泛地关注。保护生物多样性的最有效的生物技术之一是建立种子基因库,进行迁地保护。种子库理想的贮藏条件主要取决于种子含水量、贮藏环境(如温度和湿度)和贮存种子的容器。进行种子贮藏,了解种子生命力和活力的影响因子的作用机理是十分重要和必要的。除了种子自身的生理特征外,种子的贮藏寿命与种子成熟度、收获技术、加工处理方法也是息息相关的。即使在最适的库存条件下,种子也会随时间发生劣变。因此,必须根据种子特定的贮藏行为,加以考虑影响种子存活的3个主要方面(贮藏环境、贮藏期和植物种类)而选择有效的贮藏方案。本文试图讨论种子贮藏生理的几个重要方面及其需解决的技术问题,以便更好地通过种子基因库,长期有效地保存植物种质资源。     

Eliciting and integrating expert knowledge to assess the viability of the critically endangered golden sun‐moth Synemon plana

The critically endangered golden sun‐moth Synemon plana occurs in urban fringe areas of southeastern Australia that are currently experiencing rapid and extensive development. The urban fringe is a complex and uncertain environment in which to manage threatened species with the intersection of fragmented natural habitats, built environments and human populations generating novel, poorly understood interactions. In this context, management frameworks must incorporate ecological processes as well as social considerations. Here, we explore how biodiversity sensitive urban design might improve the fate of the golden sun‐moth, and threatened species generally, in urban fringe environments. We: (i) developed an expert‐informed Bayesian Belief Network model that synthesizes the current understanding of key determinants of golden sun‐moth population viability at sites experiencing urbanizing pressure; (ii) quantified the nature and strength of cause‐effect relationships between these factors using expert knowledge; and (iii) used the model to assess expectations of moth population viability in response to different combinations of management actions. We predict that adult survival, bare ground cover and cover of resource plants are the most important variables affecting the viability of golden sun‐moth populations. We also demonstrate the potential for biodiversity sensitive urban design as a complementary measure to conventional management for this species. Our findings highlight how expert knowledge may be a valuable component of conservation management, especially in addressing uncertainty around conservation decisions when empirical data are lacking, and how structured expert judgements become critical in supporting decisions that may help ameliorate extinction risks faced by threatened species in urban environments.     

海洋岛屿生物多样性保育研究进展

海洋岛屿生态系统因具有明显的海域地理隔离而区别于陆地生态系统,被誉为生物地理与进化生态学研究的"天然实验室".陆地或其它邻近岛屿的种源物种迁移到新的岛屿后,经历地理隔离、特征置换或适应辐射等一系列的岛屿进化过程,形成与种源物种具有显著遗传差异的岛屿特有种.岛屿在小面积范围内分化形成大量的特有种,是岛屿生物多样性最为重要的特点之一.但是,岛屿种群由于分布范围局限、生境脆弱且种群规模较小,岛屿种群较陆地种群具有更高的灭绝风险.本文通过对海洋岛屿物种的起源与演化、遗传结构以及岛屿物种的濒危与保护3个热点问题的讨论,阐述岛屿生物多样性的形成机制、濒危肇因以及岛屿生物多样性保育的重要性.     

Overcoming barriers to active interventions for genetic diversity

As a result of processes such as habitat loss and overharvest, many species persist in small, isolated populations that experience reduced fitness, decreased evolutionary potential, and increased extinction risk. The goal of species conservation is to restore genetic diversity and adaptive potential caused by isolation and small population size. For populations trapped in an extinction vortex, habitat protection may be inadequate for successful conservation. Alternative actions such as deliberate admixture by introducing individuals from related subspecies may be necessary to recover population fitness. While there is precedent for such actions, admixture temporarily disrupts the taxonomic integrity of a species. Concerns for the taxonomic integrity or “naturalness” of a species may prevent the use of active interventions that involve admixture and transient hybrid gene pools even though extinction may be imminent. We explore the cultural barriers to using tools such as genetic rescue and make suggestions for overcoming those barriers. We focus mainly on examples from animals, but the same evolutionary processes are ongoing in other life forms and are subject to the same cultural barriers.     

Sex and recombination purge the genome of deleterious alleles: An Individual Based Modeling Approach

The prevalence of sexual reproduction in most animal species despite its considerable costs such as useless males, energy spent on mating, the cost of meiosis and genome dilution remains a puzzle in evolutionary theory. One prominent single factor attempt to solve this persistent puzzle is the claim that sexual reproduction is instrumental in eliminating deleterious alleles from the species genome by the mechanism of recombination. There are three major versions of the deleterious allele hypothesis: First, the mutational deterministic hypothesis (MDH), which rests on the assumption of negative epistasis, predicts that recombination will help to purge the species genome of deleterious alleles by breaking apart linkages between these alleles. The assumption is that the joint negative effects of linked deleterious alleles is sometimes greater than the effects of the alleles considered separately. Second, there is the hypothesis that sexual reproduction speeds up purifying (negative) selection, which purges the genome of deleterious alleles. Alleles that are less deleterious than the wild type are naturally selected. These alleles, attained via recombination, are sometimes ‘leaky’ mutations giving rise to reduced functionality of attendant proteins. This hypothesis does not necessarily rest on the assumption of negative epistasis, which some argue is relatively rare in nature (Kouyos, Silander and Bonhoeffer (2012)) and which arguably could be seen as a virtue of the purifying selection hypothesis vs. the MDH. Third, Muller's ratchet hypothesis predicts that recombination will help to prevent the buildup of deleterious mutations by the mechanism of recombination. In this study, we focus primarily on testing the purifying selection hypothesis. We performed an individual-based model computer simulation using the program EcoSim to test this hypothesis. The experimental runs for sexual reproduction, asexual reproduction and facultative reproduction involved introducing a deleterious allele into the genome, which exacts an intermediate-level energy penalty on individuals. It was found that whereas on average, deleteriousness consistently declined over 18,000 time-steps due to recombination in sexual reproduction, deleteriousness did not decline for asexual and facultative runs. These results corroborate the hypothesis that recombination due to sexual reproduction helps to eliminate deleterious alleles from the genome through the selection of reduced function mutations.     

Deep Learning for Population Genetic Inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.     

Population genetic suggestions to offset the extinction ratchet in the endangered Canarian endemic Atractylis preauxiana (Asteraceae)

We examined the levels and apportionment of genetic variation of the 11 known subpopulations of Atractylis preauxiana at 95 RAPD loci to help streamline a conservation strategy for this Canarian endemic taxon, which is in a critical situation because of the constant exposure of plants to intensive, uncontrolled anthropic action in the last few decades. Our results revealed low genetic variation levels that match with the general picture of demographic and habitat degradation that this taxon is undergoing. Although geographic isolation between Tenerife and Gran Canaria is an effective barrier to gene flow, genetic heterogeneity within islands is also substantial, plausibly due to the negative impact of fragmentation on genetic variation. Our genetic results, together with declining population sizes, poor seedling survival, and recent population extinctions, compellingly indicate that A. preauxiana is undergoing an extinction ratchet, whereby every further local extinction will add up to the probability of total species’ extinction. Our genetic results suggest that mitigating the deleterious consequences of this effect entails urgent mixed reinforcements of all sub-populations with sub-populations from the same island and urgent translocation of the two sub-populations from Tenerife that are doomed to extinction to ecologically suitable areas, together with seed collection and preservation in a convenient ex situ banking facility.     

Conservation implications of hybridization in Hawaiian picture-winged Drosophila

In this review, we discuss the importance of hybridization among species for the conservation of Hawaiian picture-winged Drosophila. Hybridization can be a positive evolutionary process that creates new species and increases the adaptation of populations and species through the spread of adaptive alleles and traits. Conversely, hybridization can disrupt the genetic integrity of species or populations and this may be most detrimental among taxa that are recently hybridizing due to recent ecological changes. The loss of biodiversity in Hawaiian Drosophila through hybridization may be facilitated by habitat alteration and introduced species that reduce population sizes and alter geographic distributions of native species. We briefly review the evidence for hybridization in the genus Drosophila and then focus on hybridization in the Hawaiian picture-winged Drosophila. We examine three general approaches for identifying hybrids and for assessing the factors that appear to contribute to hybridization and the potential ecological and evolutionary outcomes of hybrids in natural populations. Overall, the potential for hybridization among species will likely increase the risk of extinction for Hawaiian picture-winged Drosophila species. Thus, it is important to consider the potential for hybridization among species when developing plans for the conservation of Hawaiian Drosophila.     

Potential distribution of vulnerable Entandrophragma angolense (Welw.) C. DC. (Meliaceae) in East Africa

The various human‐induced threats imposed on nature have recently triggered the study of species' distributions. We developed potential suitability models using two algorithms for a threatened African mahogany, Entandrophragma angolense, in three East African countries; Kenya, Tanzania and Uganda. The effect of features selection and modelling algorithm selection on potential suitability predictions was explored. Occurrence records and high‐resolution environmental data were used. The two species distribution modelling techniques were genetic algorithm rule for prediction; and maximum entropy modelling. With Maxent, the area under the receiver characteristic operating curve (AUC) for potential distribution models tested on independent data ranged from 0.942 to 0.972 when using automatic features and from 0.974 to 0.666 with target or specific features. With GARP, AUC for potential distribution models ranged from 0.591 to 0.736 with all rule types and from 0.388 to 0.805 for specific rule types (Tables  1  and 2 ). The area under the E. angolense potential suitability was best predicted by soil, rainfall and aspect using GARP. Potential suitability increased with increasing aspect and decreased with increasing slope. Low rainfall and elevation increased potential suitability, while high levels of either variable decreased potential suitability. Potential suitability maps for vulnerable species require using a multi‐algorithm, fine scale data approach and incorporation of environmental variables like soil, slope, land use and elevation. Species distribution models can offer insight on the distribution requirements of vulnerable species and help guide the development of management plans. Results of this study suggest that E. angolense management plans should promote the protection of terrestrial forests surrounding water bodies including Mabira forest in Uganda.