Modelos de distribución de especies y estado de conservación de murciélagos amenazados en la región tumbesina de Ecuador y Perú / Species distribution models and conservation status of threatened bats in the Tumbesian region of Ecuador and Perú

Resumen Introducción: La biodiversidad se está perdiendo a un ritmo acelerado como resultado del cambio global. Herramientas como los modelos de distribución de especies (MDEs) han sido ampliamente usados para mejorar el conocimiento sobre el estado de conservación de las especies y ayudar a desarrollar estrategias de gestión para mitigar la pérdida de biodiversidad. Objetivo: Determinar cómo la distribución potencial predicha por los MDEs para ocho especies de murciélagos amenazados difiere de los mapas de distribución reportados por la UICN. También, inferir el área de distribución y estado de endemismo de cada especie, y evaluar la importancia de la región tumbesina para su conservación. Métodos: Basados en registros de presencia del rango global de las especies, usamos MDEs para evaluar el estado de conservación de estas ocho especies en la región tumbesina de Ecuador y Perú. Resultados: Las áreas estimadas por los MDEs eran 35-78 % más pequeñas para cuatro especies (Eptesicus innoxius, Lophostoma occidentale, Platalina genovensium y Lonchophylla hesperia) y 26-1 600 % más grandes para tres especies (Amorphochilus schnablii, Promops davisoni y Rhogeessa velilla) que aquellas reportadas por la UICN. Para Tomopeas ravus, el área estimada por el MDE y la UICN fue similar, pero difirió en la distribución espacial. Los MDEs coincidieron con áreas de endemismo informadas por autores previos para E. innoxius, R. velilla y T. ravus, pero fueron diferentes para A. schnablii, P. genovensium, P. davisoni y L. hesperia, debido en parte a las distribuciones proyectadas para estas últimas especies en valles secos interandinos según los MDEs. Conclusiones: La región tumbesina representa una porción significativa (40-96 %) de la distribución predicha de siete de las ocho especies estudiadas, subrayando la importancia de esta región para la conservación de murciélagos. Nuestros resultados muestran las probables distribuciones para estas especies y proporcionan una base importante para identificar vacíos de investigación y desarrollar medidas de conservación para murciélagos amenazados en el punto caliente de biodiversidad de Tumbes.

Abstract Introduction: Biodiversity is being lost at an accelerating rate because of global change. Tools such as species distribution models (SDMs) have been widely used to improve knowledge about species' conservation status and help develop management strategies to mitigate biodiversity loss. SDMs are especially important for species with restricted distributions, such as endemic species. Objective: To determine how potential distribution predicted by SDMs for eight threatened bat species differed from the distribution maps reported by the IUCN. Also, to infer the area of distribution and state of endemism of each specie, and to evaluate the importance of the Tumbesian region for their conservation. Methods: Based on presence records across the species' entire ranges, we used SDMs to assess the conservation status of these eight species in the Tumbesian region of Ecuador and Peru. Results: The areas estimated by SDMs were 35-78 % smaller for four species (Eptesicus innoxius, Lophostoma occidentale, Platalina genovensium and Lonchophylla hesperia) and 26-1 600 % larger for three species (Amorphochilus schnablii, Promops davisoni and Rhogeessa velilla) than those reported by the IUCN. For Tomopeas ravus, the area estimated by the SDM and IUCN was similar but differed in spatial distribution. SDMs coincided with areas of endemism reported by previous authors for E. innoxius, R. velilla, and T. ravus, but were different for A. schnablii, P. genovensium, P. davisoni, and L. hesperia, due in part to projected distributions for these latter species in dry inter-Andean valleys according to the SDMs. Conclusions: The Tumbesian region represents a significant portion (40-96 %) of the predicted distribution of seven of the eight species studied, underscoring the importance of this region for bat conservation. Our results show likely distributions for these species and provide an important basis for identifying research gaps and developing conservation measures for threatened bats in the Tumbes biodiversity hotspot.

Accurate space-group prediction from composition.

Predicting crystal symmetry simply from chemical composition has remained challenging. Several machine-learning approaches can be employed, but the predictive value of popular crystallographic databases is relatively modest due to the paucity of data and uneven distribution across the 230 space groups. In this work, virtually all crystallographic information available to science has been compiled and used to train and test multiple machine-learning models. Composition-driven random-forest classification relying on a large set of descriptors showed the best performance. The predictive models for crystal system, Bravais lattice, point group and space group of inorganic compounds are made publicly available as easy-to-use software downloadable from https://gitlab.com/vishsoft/cosy.

Isolated, neglected, and likely threatened: a new species of Magoniella (Polygonaceae) from the seasonally dry tropical forests of Northern Colombia and Venezuela revealed from nuclear, plastid, and morphological data.

Seasonally tropical dry forests (SDTFs) in the American tropics are a highly diverse yet poorly understood and endangered ecosystem scattered from Northern Mexico to Southern Argentina. One floristic element of the STDFs is the genus Magoniella (Polygonaceae), which includes two liana species, M. laurifolia and M. obidensis, which have winged fruits and are distributed from Costa Rica to Southern Brazil. In a field expedition to the SDTFs of the Colombian Caribbean in 2015, morphologically distinctive individuals of Magoniella were found. In this study, we investigated the species boundaries within Magoniella and determined the phylogenetic position of these morphologically distinctive individuals in the tribe Triplaridae. We compiled morphological trait data across 19 specimens of both species and produced newly sequenced nuclear-plastid DNA data for M. obidensis. Morphometric analyses revealed significant differences in fruit length and perianth size among individuals from the Colombian Caribbean compared to M. obidensis and bract length when compared to M. laurifolia. Maximum likelihood analysis of non-conflicting nuclear and plastid datasets placed the Colombian Caribbean individuals as sister to M. obidensis with maximum statistical support. Additionally, pairwise sequence comparisons of the nuclear ribosomal ITS and the lfy2i loci consistently showed 15-point mutations (10 transitions, five transversions) and six 2 bp-long substitutions that differ between M. obidensis and the Colombian Caribbean individuals. Our morphological and molecular evidence thus suggests that the Colombian Caribbean individuals of Magoniella represent a divergent population from M. laurifolia and M. obidensis, which we describe and illustrate as a new species, M. chersina. Additionally, we provide nomenclatural updates for M. laurifolia and M. obidensis. This study highlights the power of combining morphological and molecular evidence in documenting and naming plant diversity.

The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in the past decades, the overall survival (OS) of liver cancer is still disappointing. Machine learning models have several advantages over traditional cox models in prognostic prediction. This study aimed at designing an optimal panel and constructing an optimal machine learning model in predicting prognosis for HCC. A total of 941 HCC patients with completed survival data and preoperative clinical chemistry and immunology indicators from two medical centers were included. The OCC panel was designed by univariate and multivariate cox regression analysis. Subsequently, cox model and machine-learning models were established and assessed for predicting OS and PFS in discovery cohort and internal validation cohort. The best OCC model was validated in the external validation cohort and analyzed in different subgroups. In discovery, internal and external validation cohort, C-indexes of our optimal OCC model were 0.871 (95% CI, 0.863-0.878), 0.692 (95% CI, 0.667-0.717) and 0.648 (95% CI, 0.630-0.667), respectively; the 2-year AUCs of OCC model were 0.939 (95% CI, 0.920-0.959), 0.738 (95% CI, 0.667-0.809) and 0.725 (95% CI, 0.643-0.808), respectively. For subgroup analysis of HCC patients with HBV, aged less than 65, cirrhosis or resection as first therapy, C-indexes of our optimal OCC model were 0.772 (95% CI, 0.752-0.792), 0.769 (95% CI, 0.750-0.789), 0.855 (95% CI, 0.846-0.864) and 0.760 (95% CI, 0.741-0.778), respectively. In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.

Land use and land cover changes, and woody vegetation status of the Tsimur Gebriel Monastery in Northern Ethiopia.

Ethiopian Orthodox churches are significant habitats for endemic and threatened plant species, yet their vegetation status and the land use systems impacting them, are little known. Therefore, this study assessed the land use and land cover changes (LULCC) within a 3 km buffer area and the woody vegetation status of the Tsimur Gebriel Monastery in the Tigray region, Northern Ethiopia. The United States Geological Survey's multi-dated (1986, 1999, and 2018) Landsat imagery was used for LULCC analysis. A supervised classification technique was employed for image classification using a maximum likelihood algorithm. Systematic sampling techniques were used to collect vegetation data (tree species, height, and DBH), using 20 sample plots (20 × 20 m) distanced 100 m apart. The results highlighted that among the five identified LULCC types in the buffer zone of the monastery, the farmland area has expanded from 56 to 78 % at the expense of shrublands between 1986 and 2018. At the monastery, 19 woody tree species from 13 families were identified, with an evenness of 0.5 and a Shannon diversity index of 2.4. The stem density was 336 stems per hectare, and the forest cover was approximately 65 %. Olea europaea was the dominant tree species, while Juniperus procera showed a lack of regeneration at the monastery. Despite the fair natural regeneration, the monastery exhibited lower species diversity, richness, and evenness. However, the monastery remains an important habitat for rare and threatened tree species and may supply seeds for the restoration of degraded lands. Therefore, establishing exclosures in the buffer zone, strengthening stone walls and enrichment planting of degraded tree species should be implemented to ensure the sustainable conservation of valuable tree species.

Terrain, oceanographic, and biological factors underlying the development of Mediterranean coastal animal forests.

Marine Animal Forests (MAFs) form three-dimensional seascapes and provide substrate and shelter for a variety of species. We investigated the fine-scale distribution pattern of three habitat-forming species of the coastal Mediterranean MAFs: Eunicella cavolini, E. singularis and Paramuricea clavata, and assessed the influence of terrain, oceanographic, and biological factors on their distribution and the formation of MAFs in the central-northern Tyrrhenian Sea. Species presence and abundance were obtained through seafloor HD imagery and were combined with terrain and oceanographic parameters extracted from remote sensing data using distance-based linear modeling (DistLM) and generalized additive model (GAM). The three studied species occurred in all the study areas, with marked differences in their abundance and distribution across the different sites and habitat type, in relation to seafloor characteristics. Specifically, positive relationships emerged between the density of colonies and terrain parameters indicative of high seafloor complexity, such as slope and roughness, as well as the number species structuring MAFs. A clear niche separation for the three species was observed: E. cavolini and P. clavata were reported on coralligenous reefs, and in areas where the seafloor complexity may enhance hydrodynamics and transport of organic matter, while E. singularis was observed on red algal mats at shallower depths. A better understanding of the ecology of these gorgonians, as well as of the drivers determining MAFs formation, represent the first step toward the conservation of these threatened habitats which are currently poorly protected by management and conservation plans.

Interspecific competition shapes bird species' distributions along tropical precipitation gradients.

The hypothesis that species' ranges are limited by interspecific competition has motivated decades of debate, but a general answer remains elusive. Here we test this hypothesis for lowland tropical birds by examining species' precipitation niche breadths. We focus on precipitation because it-not temperature-is the dominant climate variable that shapes the biota of the lowland tropics. We used 3.6 million fine-scale citizen science records from eBird to measure species' precipitation niche breadths in 19 different regions across the globe. Consistent with the predictions of the interspecific competition hypothesis, multiple lines of evidence show that species have narrower precipitation niches in regions with more species. This means species inhabit more specialized precipitation niches in species-rich regions. We predict this niche specialization should make tropical species in high diversity regions disproportionately vulnerable to changes in precipitation regimes; preliminary empirical evidence is consistent with this prediction.

Random forests for the analysis of matched case-control studies.

BACKGROUND: Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case-control studies are missing because conventional machine learning methods cannot handle the matched structure of the data. RESULTS: A random forest method for the analysis of matched case-control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case-control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer. CONCLUSIONS: The proposed random forest method is a promising add-on to the toolbox for the analysis of matched case-control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

Identifying early language predictors: A replication of Gasparini et al. (2023) confirming applicability in a general population cohort.

BACKGROUND: Identifying language disorders earlier can help children receive the support needed to improve developmental outcomes and quality of life. Despite the prevalence and impacts of persistent language disorder, there are surprisingly no robust predictor tools available. This makes it difficult for researchers to recruit young children into early intervention trials, which in turn impedes advances in providing effective early interventions to children who need it. AIMS: To validate externally a predictor set of six variables previously identified to be predictive of language at 11 years of age, using data from the Longitudinal Study of Australian Children (LSAC) birth cohort. Also, to examine whether additional LSAC variables arose as predictive of language outcome. METHODS & PROCEDURES: A total of 5107 children were recruited to LSAC with developmental measures collected from 0 to 3 years. At 11-12 years, children completed the Clinical Evaluation of Language Fundamentals, 4th Edition, Recalling Sentences subtest. We used SuperLearner to estimate the accuracy of six previously identified parent-reported variables from ages 2-3 years in predicting low language (sentence recall score ≥ 1.5 SD below the mean) at 11-12 years. Random forests were used to identify any additional variables predictive of language outcome. OUTCOMES & RESULTS: Complete data were available for 523 participants (52.20% girls), 27 (5.16%) of whom had a low language score. The six predictors yielded fair accuracy: 78% sensitivity (95% confidence interval (CI) = [58, 91]) and 71% specificity (95% CI = [67, 75]). These predictors relate to sentence complexity, vocabulary and behaviour. The random forests analysis identified similar predictors. CONCLUSIONS & IMPLICATIONS: We identified an ultra-short set of variables that predicts 11-12-year language outcome with 'fair' accuracy. In one of few replication studies of this scale in the field, these methods have now been conducted across two population-based cohorts, with consistent results. An imminent practical implication of these findings is using these predictors to aid recruitment into early language intervention studies. Future research can continue to refine the accuracy of early predictors to work towards earlier identification in a clinical context. WHAT THIS PAPER ADDS: What is already known on the subject There are no robust predictor sets of child language disorder despite its prevalence and far-reaching impacts. A previous study identified six variables collected at age 2-3 years that predicted 11-12-year language with 75% sensitivity and 81% specificity, which warranted replication in a separate cohort. What this study adds to the existing knowledge We used machine learning methods to identify a set of six questions asked at age 2-3 years with ≥ 71% sensitivity and specificity for predicting low language outcome at 11-12 years, now showing consistent results across two large-scale population-based cohort studies. What are the potential or clinical implications of this work? This predictor set is more accurate than existing feasible methods and can be translated into a low-resource and time-efficient recruitment tool for early language intervention studies, leading to improved clinical service provision for young children likely to have persisting language difficulties.

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure.

For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered "maximally consistent with harm." The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.

The Complex Genetic Legacy of Hybridization and Introgression between the Rare Ocotea loxensis van der Werff and the Widespread O. infrafoveolata van der Werff (Lauraceae).

Hybridization and introgression are complex evolutionary mechanisms that can increase species diversity and lead to speciation, but may also lead to species extinction. In this study, we tested the presence and genetic consequences of hybridization between the rare and Ecuadorian endemic O. loxensis van der Werff and the widespread O. infrafoveolata van der Werff (Lauraceae). Phenotypically, some trees are difficult to identify, and we expect that some might in fact be cryptic hybrids. Thus, we developed nuclear microsatellites to assess the existence of hybrids, as well as the patterns of genetic diversity and population structure in allopatric and sympatric populations. The results revealed high levels of genetic diversity, even in the rare O. loxensis, being usually significantly higher in sympatric than in allopatric populations. The Bayesian assignment of individuals into different genetic classes revealed a complex scenario with different hybrid generations occurring in all sympatric populations, but also in allopatric ones. The absence of some backcrossed hybrids suggests the existence of asymmetric gene flow, and that some hybrids might be more fitted than others might. The existence of current and past interspecific gene flow also explains the blurring of species boundaries in these species and could be linked to the high rates of species found in Ocotea.

Neighborhood Diversity Promotes Tree Growth in a Secondary Forest: The Interplay of Intraspecific Competition, Interspecific Competition, and Spatial Scale.

Understanding the biodiversity-productivity relationship (BPR) is crucial for biodiversity conservation and ecosystem management. While it is known that diversity enhances forest productivity, the underlying mechanisms at the local neighborhood level remain poorly understood. We established a 9.6 ha dynamic forest plot to study how neighborhood diversity, intraspecific competition, and interspecific competition influence tree growth across spatial scales using linear mixed-effects models. Our analysis reveals a significant positive correlation between neighborhood species richness (NSR) and relative growth rate (RGR). Notably, intraspecific competition, measured by conspecific neighborhood density and resource competition, negatively impacts RGR at finer scales, indicating intense competition among conspecifics for limited resources. In contrast, interspecific competition, measured by heterospecific density and resource competition, has a negligible impact on RGR. The relative importance of diversity and intra/interspecific competition in influencing tree growth varies with scale. At fine scales, intraspecific competition dominates negatively, while at larger scales, the positive effect of NSR on RGR increases, contributing to a positive BPR. These findings highlight the intricate interplay between local interactions and spatial scale in modulating tree growth, emphasizing the importance of considering biotic interactions and spatial variability in studying BPR.

Responses of particulate and mineral-associated organic carbon to temperature changes and their mineral protection mechanisms: A soil translocation experiment.

Mineral protection mechanisms are important in determining the response of particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) to temperature changes. However, the underlying mechanisms for how POC and MAOC respond to temperature changes are remain unclear. By translocating soils across 1304 m, 1425 m and 2202 m elevation gradient in a temperate forest, simulate nine months of warming (with soil temperature change of +1.41 °C and +3.91 °C) and cooling (with soil temperature change of -1.86 °C and -4.20 °C), we found that warming translocation significantly decreased POC by an average of 10.84 %, but increased MAOC by an average of 4.25 %. Conversely, cooling translocation led to an average increase of 8.64 % in POC and 13.48 % in MAOC. Exchangeable calcium (Caexe) had a significant positive correlation with POC and MAOC during temperature changes, and Fe/Al-(hydr)oxides had no significant correlation or a significant negative correlation with POC and MAOC. Our results showed that POC was more sensitive than MAOC to temperature changes. Caexe mediated the stability of POC and MAOC under temperature changes, and Fe/Al-(hydr)oxides had no obvious protective effect on POC and MAOC. Our results support the role of mineral protection in the stabilization mechanism of POC and MAOC in response to climate change and are critical for understanding the consequences of global change on soil organic carbon (SOC) dynamics.

Lithology and elevated temperature impact phoD-harboring bacteria on soil available P enhancing in subtropical forests.

Plants are generally limited by soil phosphorus (P) deficiency in forest ecosystems. Soil available P is influenced by lithology, temperature, and soil microbes. However, the interactive effects of these factors on soil P availability in subtropical forests remain unclear. To assess their impacts, we measured soil inorganic and available P fractions and the diversity, composition, and co-occurrence network of phoD-harboring bacteria in two contrasting forest soils (lithosols in karst forests and ferralsols in non-karst forests) in the subtropical regions of southwestern China across six temperature gradients. The present results showed that the complexities in composition and network and the diversity indices of phoD-harboring bacteria were higher in the karst forest soils than those in the non-karst forest soils, with marked differences in composition. In both types of forest soils, the complexities of composition and networks and the diversity indices were higher in the high-temperature regions (mean annual temperature (MAT) > 16 °C) compared to the low-temperature regions (MAT <16 °C). Soil total inorganic and available P contents were lower in the karst forest soils compared to the non-karst forest soils. Soil total available P contents were lower in the high temperature regions than those in the low temperature regions in both forest soils, whereas soil total inorganic P contents were contrary. Variance partitioning analysis showed that soil inorganic and available P fractions were predominantly explained by lithology and its interaction with soil microbes and climate. The present findings demonstrate that soil P availability in subtropical forests of southwestern China is influenced by lithology and temperature, which regulate the diversity, composition, and network connectivity of phoD-harboring bacteria. Furthermore, this study highlights the significance of controlling the composition of phoD-harboring bacteria for mitigating plant P deficiency in karst ecosystems.

Long-term Trends of Dissolved Organic Carbon Dynamics in a Subtropical Forest Responding to Environmental Changes.

Dissolved organic carbon (DOC) dynamics are critical to carbon cycling in forest ecosystems and sensitive to global change. Our study, spanning from 2001 to 2020 in a headwater catchment in subtropical China, analyzed DOC and water chemistry of throughfall, litter leachate, soil waters at various depths, and streamwater. We focused on DOC transport through hydrological pathways and assessed the long-term trends in DOC dynamics amidst environmental and climatic changes. Our results showed that the annual DOC deposition via throughfall and stream outflow was 14.2 ± 2.2 and 1.87 ± 0.83 g C m-2 year-1, respectively. Notably, there was a long-term declining trend in DOC deposition via throughfall (-0.195 mg C L-1 year-1), attributed to reduced organic carbon emissions from clean air actions. Conversely, DOC concentrations in soil waters and stream waters showed increasing trends, primarily due to mitigated acid deposition. Moreover, elevated temperature and precipitation could partly explain the long-term rise in DOC leaching. These trends in DOC dynamics have significant implications for the stability of carbon sink in terrestrial, aquatic, and even oceanic ecosystems at regional scales.

Analysis of the Correspondence of the Degree of Fragility with the Way to Exercise the Force of the Hand.

BACKGROUND: Frailty is a geriatric syndrome characterized by increased individual vulnerability with an increase in both dependence and mortality when exposed to external stressors. The use of Frailty Indices in routine clinical practice is limited by several factors, such as the cognitive status of the patient, times of consultation, or lack of prior information from the patient. OBJECTIVES: In this study, we propose the generation of an objective measure of frailty, based on the signal from hand grip strength (HGS). DESIGN AND MEASUREMENTS: This signal was recorded with a modified Deyard dynamometer and processed using machine learning strategies based on supervised learning methods to train classifiers. A database was generated from a cohort of 138 older adults in a transverse pilot study that combined classical geriatric questionnaires with physiological data. PARTICIPANTS: Participants were patients selected by geriatricians of medical services provided by collaborating entities. SETTING AND RESULTS: To process the generated information 20 selected significant features of the HGS dataset were filtered, cleaned, and extracted. A technique based on a combination of the Synthetic Minority Oversampling Technique (SMOTE) to generate new samples from the smallest group and ENN (technique based on K-nearest neighbors) to remove noisy samples provided the best results as a well-balanced distribution of data. CONCLUSION: A Random Forest Classifier was trained to predict the frailty label with 92.9% of accuracy, achieving sensitivities higher than 90%.

A genome-guided strategy for climate resilience in American chestnut restoration populations.

American chestnut (Castanea dentata) is a deciduous tree species of eastern North America that was decimated by the introduction of the chestnut blight fungus (Cryphonectria parasitica) in the early 20th century. Although millions of American chestnuts survive as root collar sprouts, these trees rarely reproduce. Thus, the species is considered functionally extinct. American chestnuts with improved blight resistance have been developed through interspecific hybridization followed by conspecific backcrossing, and by genetic engineering. Incorporating adaptive genomic diversity into these backcross families and transgenic lines is important for restoring the species across broad climatic gradients. To develop sampling recommendations for ex situ conservation of wild adaptive genetic variation, we coupled whole-genome resequencing of 384 stump sprouts with genotype-environment association analyses and found that the species range can be subdivided into three seed zones characterized by relatively homogeneous adaptive allele frequencies. We estimated that 21 to 29 trees per seed zone will need to be conserved to capture most extant adaptive diversity. We also resequenced the genomes of 269 backcross trees to understand the extent to which the breeding program has already captured wild adaptive diversity, and to estimate optimal reintroduction sites for specific families on the basis of their adaptive portfolio and future climate projections. Taken together, these results inform the development of an ex situ germplasm conservation and breeding plan to target blight-resistant breeding populations to specific environments and provides a blueprint for developing restoration plans for other imperiled tree species.

Limited drought tolerance in the neotropical seasonally dry forest plants impairs future species richness.

Neotropical seasonal dry forest (NSDF) is one of the most threatened ecosystems according to global climate change predictions. Nonetheless, few studies have evaluated the global climate change impacts on diversity patterns of NSDF plants. The lack of whole biome-scale approaches restricts our understanding of global climate change consequences in the high beta-diverse NSDF. We analysed the impact of global climate change on species distribution ranges, species richness, and assemblage composition (beta diversity) for 1,178 NSDF species. We used five representative plant families (in terms of abundance, dominance, and endemism) within the NSDF: Cactaceae, Capparaceae, Fabaceae, Malvaceae, and Zygophyllaceae. We reconstructed potential species distributions in the present and future (2040-2080), considering an intermediate Shared Socioeconomic Pathway and two dispersal ability assumptions on the taxa. Using a resource use scores index, we related climate-induced range contractions with species' water stress tolerance. Even under a favourable dispersal scenario, species distribution and richness showed future significant declines across those sites where mean temperature and precipitation seasonality are expected to increase. Further, changes in species range distribution in the future correlated positively with potential use of resources in Fabaceae. Results suggest that biotic heterogenization will likely be the short-term outcome at biome scale under dispersal limitations. Nonetheless, by 2080, the prevailing effect under both dispersal assumptions will be homogenization, even within floristic nuclei. This information is critical for further defining new areas worth protecting and future planning of mitigation actions for both species and the whole biome.

Explaining the emergence of land-use frontiers.

Land-use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called 'frontiers', defined here as places experiencing marked transformations owing to rapid resource exploitation. Understanding the mechanisms shaping these frontiers is crucial for sustainability. Previous work focused mainly on explaining how active frontiers advance, in particular, into tropical forests. Comparatively, our understanding of how frontiers emerge in territories considered marginal in terms of agricultural productivity and global market integration remains weak. We synthesize conceptual tools explaining resource and land-use frontiers, including theories of land rent and agglomeration economies, of frontiers as successive waves, spaces of territorialization, friction and opportunities, anticipation and expectation. We then propose a new theory of frontier emergence, which identifies exogenous pushes, legacies of past waves and actors' anticipations as key mechanisms by which frontiers emerge. Processes of differential rent creation and capture and the built-up of agglomeration economies then constitute key mechanisms sustaining active frontiers. Finally, we discuss five implications for the governance of frontiers for sustainability. Our theory focuses on agriculture and deforestation frontiers in the tropics but can be inspirational for other frontier processes including for extractive resources, such as minerals.

Chloroplast genome-based genetic resources via genome skimming for the subalpine forests of Japan and adjacent regions.

The Japanese subalpine zone is dominated by an ecologically important forest biome, subalpine coniferous forest, constituting a distinct assemblage of cold-tolerant angiosperm and conifer species. While being relatively intact compared to other forest biomes in Japan, subalpine coniferous forests are under significant threat from deer browsing, global warming and small population size effects. However, there is a severe lack of genetic resources available for this biome's major constituent plant species. This study aimed to develop chloroplast genome-based genetic resources for 12 widespread subalpine tree and shrub species (7 angiosperms and 5 conifers) via genome skimming of whole-genomic DNA using short reads (100-150 bp in length). For 10 species, whole chloroplast genomes were assembled via de novo-based methods from 4 to 10 individuals per species sampled from across their ranges in Japan and, for non-Japanese endemic species, elsewhere in northeast Asia. A total of 566 single nucleotide polymorphisms for Japanese samples and 768 for all samples (varying from 2 to 202 per species) were identified which were distributed in geographically restricted lineages in most species. In addition, between 9 and 58 polymorphic simple sequence repeat regions were identified per species. For two Ericaceae species (Rhododendron brachycarpum and Vaccinium vitis-idaea) characterised by large chloroplast genomes, de novo assembly failed, but single nucleotide polymorphisms could be identified using reference mapping. These data will be useful for genetic studies of species taxonomic relationships, investigating phylogeographic patterns within species, developing chloroplast-based markers for conservation genetic studies and has potential application for studies of environmental and ancient DNA.