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Madagascar’s biota is hyperdiverse and includes exceptional levels of endemicity. We review the current state of knowledge on Madagascar’s past and current terrestrial and freshwater biodiversity by compiling and presenting comprehensive data on species diversity, endemism, and rates of species description and human uses, in addition to presenting an updated and simplified map of vegetation types. We report a substantial increase of records and species new to science in recent years; however, the diversity and evolution of many groups remain practically unknown (e.g., fungi and most invertebrates). Digitization efforts are increasing the resolution of species richness patterns and we highlight the crucial role of field- and collections-based research for advancing biodiversity knowledge and identifying gaps in our understanding, particularly as species richness corresponds closely to collection effort. Phylogenetic diversity patterns mirror that of species richness and endemism in most of the analyzed groups. We highlight humid forests as centers of diversity and endemism because of their role as refugia and centers of recent and rapid radiations. However, the distinct endemism of other areas, such as the grassland-woodland mosaic of the Central Highlands and the spiny forest of the southwest, is also biologically important despite lower species richness. The documented uses of Malagasy biodiversity are manifold, with much potential for the uncovering of new useful traits for food, medicine, and climate mitigation. The data presented here showcase Madagascar as a unique “living laboratory” for our understanding of evolution and the complex interactions between people and nature. The gathering and analysis of biodiversity data must continue and accelerate if we are to fully understand and safeguard this unique subset of Earth’s biodiversity.

Assessing species' extinction risk is vital to setting conservation priorities. However, assessment endeavors, such as those used to produce the IUCN Red List of Threatened Species, have significant gaps in taxonomic coverage. Automated assessment (AA) methods are gaining popularity to fill these gaps. Choices made in developing, using, and reporting results of AA methods could hinder their successful adoption or lead to poor allocation of conservation resources. We explored how choice of data cleaning type and level, taxonomic group, training sample, and automation method affect performance of threat status predictions for plant species. We used occurrences from the Global Biodiversity Information Facility (GBIF) to generate assessments for species in 3 taxonomic groups based on 6 different occurrence-based AA methods. We measured each method's performance and coverage following increasingly stringent occurrence cleaning. Automatically cleaned data from GBIF performed comparably to occurrence records cleaned manually by experts. However, all types of data cleaning limited the coverage of AAs. Overall, machine-learning-based methods performed well across taxa, even with minimal data cleaning. Results suggest a machine-learning-based method applied to minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data cleaning, training sample, and automation methods depend on the study group, intended applications, and expertise.La valoración del riesgo de extinción de las especies es vital para el establecimiento de prioridades de conservación. Sin embargo, los esfuerzos de valoración, como los que se usan para generar la Lista Roja de Especies Amenazadas de la UICN, tienen brechas importantes en la cobertura taxonómica. Los métodos de valoración automatizada (VA) están ganando popularidad como reductores de estas brechas. Las elecciones realizadas en el desarrollo, uso y reporte de resultados de los métodos de VA podrían obstaculizar su adopción exitosa o derivar en una asignación deficiente de recursos para la conservación. Exploramos cómo la selección del tipo de limpieza de datos y el nivel, grupo taxonómico, muestra de entrenamiento y el método de automatización afectan el desempeño de las predicciones del estado de amenaza de las especies de plantas. Usamos los registros de la Global Biodiversity Information Facility (GBIF) para generar las valoraciones de las especies de tres grupos taxonómicos con base en seis métodos diferentes de VA basados en la presencia de las especies. Medimos el desempeño de cada método y cobertura después de una limpieza de presencia cada vez más estricta. La información de la GBIF limpiada automáticamente tuvo un desempeño comparable con los registros de presencia limpiados manualmente por expertos. Sin embargo, todos los tipos de limpieza de datos limitaron la cobertura de las valoraciones automatizadas. En general, los métodos basados en el aprendizaje automático tuvieron un buen desempeño en todos los taxones, incluso con una limpieza mínima de datos. Los resultados sugieren que un método basado en el aprendizaje automático aplicado a información con la mínima limpieza ofrece el mejor equilibrio entre el desempeño y la cobertura de la especie. A pesar de esto, la limpieza óptima de datos, la muestra de entrenamiento y los métodos de automatización dependen del grupo de estudio, las aplicaciones deseadas y la experiencia.

The genus Restrepia occurs throughout Central and South America in areas of montane forest heavily affected by deforestation. The current study was designed to test the feasibility of using availab...

The International Union for Conservation of Nature (IUCN) Red List of Threatened Species is central in biodiversity conservation, but insufficient resources hamper its long-term growth, updating, and consistency. Models or automated calculations can alleviate those challenges by providing standardised estimates required for assessments, or prioritising species for (re-)assessments. However, while numerous scientific papers have proposed such methods, few have been integrated into assessment practice, highlighting a critical research-implementation gap. We believe this gap can be bridged by fostering communication and collaboration between academic researchers and Red List practitioners, and by developing and maintaining user-friendly platforms to automate application of the methods. We propose that developing methods better encompassing Red List criteria, systems, and drivers is the next priority to support the Red List.

Madagascar’s unique biota is heavily affected by human activity and is under intense threat. Here, we review the current state of knowledge on the conservation status of Madagascar’s terrestrial and freshwater biodiversity by presenting data and analyses on documented and predicted species-level conservation statuses, the most prevalent and relevant threats, ex situ collections and programs, and the coverage and comprehensiveness of protected areas. The existing terrestrial protected area network in Madagascar covers 10.4% of its land area and includes at least part of the range of the majority of described native species of vertebrates with known distributions (97.1% of freshwater fishes, amphibians, reptiles, birds, and mammals combined) and plants (67.7%). The overall figures are higher for threatened species (97.7% of threatened vertebrates and 79.6% of threatened plants occurring within at least one protected area). International Union for Conservation of Nature (IUCN) Red List assessments and Bayesian neural network analyses for plants identify overexploitation of biological resources and unsustainable agriculture as the most prominent threats to biodiversity. We highlight five opportunities for action at multiple levels to ensure that conservation and ecological restoration objectives, programs, and activities take account of complex underlying and interacting factors and produce tangible benefits for the biodiversity and people of Madagascar.

In an era of increasing threat most species of plant still lack an assessment of extinction risk or an understanding of priority for conservation. Evolutionary distinctiveness (ED) has been used in conjunction with extinction risk to prioritise species for conservation. We apply this approach to an economically important group of plants in Africa; South African Dioscorea (yams), focusing on the Africa clade that contains species used mainly medicinally with a distinct evolutionary history. The results of IUCN Red List assessments for these species were used in subsequent EDGE (Evolutionary Distinct and Globally Endangered) analysis. Species distribution models were produced for Africa clade species using MaxEnt, which were then combined together with the EDGE scores to produce species richness (SR), extinction risk (ER), ED and EDGE diversity maps. Five out of the 15 South African Dioscorea species (all nested in the Africa clade) were found to be in a Red List threat category, and this category was shown to be the main determinant of its EDGE ranking. The spatial distribution of SR, ER, ED and EDGE were compared, and we use these maps to prioritise important areas for conservation in South Africa with hotspots identified as priorities for future conservation of Africa clade Dioscorea.

The IUCN Red List of Threatened SpeciesTM (hereafter the Red List) is an important global resource for conservation that supports conservation planning, safeguarding critical habitat and monitoring biodiversity change (Rodrigues et al. 2006). However, a major shortcoming of the Red List is that most of the world's described species have not yet been assessed and published on the Red List (Bachman et al. 2019Eisenhauer et al. 2019). Conservation efforts can be better supported if the Red List is expanded to achieve greater coverage of mega-diverse groups of organisms such as plants, fungi and invertebrates. There is, therefore, an urgent need to speed up the Red List assessment and documentation workflow. One reason for this lack of species coverage is that a manual and relatively time-consuming procedure is usually employed to assess and document species. A recent update of Red List documentation standards (IUCN 2013) reduced the data requirements for publishing non-threatened or 'Least Concern' species on the Red List. The majority of the required fields for Least Concern plant species can be found in existing open-access data sources or can be easily calculated. There is an opportunity to consolidate these data and analyses into a simple application to fast-track the publication of Least Concern assessments for plants. There could be as many as 250,000 species of plants (60%) likely to be categorised as Least Concern (Bachman et al. 2019), for which automatically generated assessments could considerably reduce the outlay of time and valuable resources for Red Listing, allowing attention and resources to be dedicated to the assessment of those species most likely to be threatened. We present a web application, Rapid Least Concern, that addresses the challenge of accelerating the generation and documentation of Least Concern Red List assessments. Rapid Least Concern utilises open-source datasets, such as the Global Biodiversity Information Facility (GBIF) and Plants of the World Online (POWO) through a simple web interface. Initially, the application is intended for use on plants, but it could be extended to other groups, depending on the availability of equivalent datasets for these groups. Rapid Least Concern users can assess a single species or upload a list of species that are assessed in a batch operation. The batch operation can either utilise georeferenced occurrence data from GBIF or occurrence data provided by the user. The output includes a series of CSV files and a point map file that meet the minimum data requirements for a Least Concern Red List assessment (IUCN 2013). The CSV files are compliant with the IUCN Red List SIS Connect system that transfers the data files to the IUCN database and, pending quality control checks and review, publication on the Red List. We outline the knowledge gap this application aims to fill and describe how the application works. We demonstrate a use-case for Rapid Least Concern as part of an ongoing initiative to complete a global Red List assessment of all native species for the United Kingdom Overseas Territory of Bermuda.

Societal Impact Statement There is increasing awareness that plants and fungi, as natural solutions, can play an important role in tackling ongoing global environmental challenges. We illustrate how understanding current and projected threats to plants and fungi is necessary to manage and mitigate risks, while building awareness of gaps and bias in current assessment coverage is essential to adequately prioritize conservation efforts. We highlight the state of the art in conservation science and point to current methods of assessment and future studies needed to mitigate species extinction. Summary Plant and fungal biodiversity underpin life on earth and merit careful stewardship in an increasingly uncertain environment. However, gaps and biases in documented extinction risks to plant and fungal species impede effective management. Formal extinction risk assessments help avoid extinctions, through engagement, financial, or legal mechanisms, but most plant and fungal species lack assessments. Available global assessments cover c. 30% of plant species (ThreatSearch). Red List coverage overrepresents woody perennials and useful plants, but underrepresents single‐country endemics. Fungal assessments overrepresent well‐known species and are too few to infer global status or trends. Proportions of assessed vascular plant species considered threatened vary between global assessment datasets: 37% (ThreatSearch), and 44% (International Union for Conservation of Nature Red List of Threatened Species). Our predictions, correcting for several quantifiable biases, suggest that 39% of all vascular plant species are threatened with extinction. However, other biases remain unquantified, and may affect our estimate. Preliminary trend data show plants moving toward extinction. Quantitative estimates based on plant extinction risk assessments may understate likely biodiversity loss: they do not fully capture the impacts of climate change, slow‐acting threats, or clustering of extinction risk, which could amplify loss of evolutionary potential. The importance of extinction risk estimation to support existing and emerging conservation initiatives is likely to grow as threats to biodiversity intensify. This necessitates urgent and strategic expansion of efforts toward comprehensive and ongoing assessment of plant and fungal extinction risk.

Identification of conservation priorities is essential for conservation planning, especially as the biodiversity crisis develops. We aimed to support conservation prioritisation by addressing knowledge gaps for the genus Aloe in the Horn of Africa. Specifically, we developed a dataset of herbarium voucher specimens and occurrence data to estimate geographic distribution of 88 species of Aloe and used this to estimate extinction risk and establish the major threats to Aloe in this region. The resulting assessments, each published on the IUCN Red List, show that 39% of the species are threatened with extinction, and the principal threats are the expansion and intensification of crop farming and livestock farming, gathering of plants, and unintentional effects of logging and wood harvesting. We review ex situ conservation in botanic gardens and seed banks, revealing gaps in coverage and urgent priorities for collection, with 25 threatened Aloe species currently unrepresented in seed banks. Protected areas in the region offer limited coverage of Aloe distributions and the most recently designated protected areas are increasingly in regions that do not overlap with Aloe distributions. However, we show with a simple optimisation approach that even a modest increase in protected area of 824 square kilometres would allow representation of all Aloe species, although further data are needed to test the area required to ensure long-term persistence (resilience) of Aloe species.

© 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. The modifiable areal unit problem is prevalent across many aspects of spatial analysis within ecology and conservation. The problem is particularly manifested when calculating metrics for extinction risk estimation, for example, area of occupancy (AOO). Although embedded in the International Union for the Conservation of Nature (IUCN) Red List criteria, AOO is often not used or is poorly applied. We evaluated new and existing methods for calculating AOO from occurrence records and devised a method for determining the minimum AOO with a uniform grid. We evaluated the grid cell shape, origin, and rotation with real-world and simulated data and reviewed the effects on AOO values and possible impacts for species already assessed on the IUCN Red List. The AOO varied by up to 80%, and a ratio of cells to points of 1:1.21 yielded the maximum variation in the number of occupied cells. These findings potentially impact 3% of existing species on the IUCN Red List and species not yet assessed. Our new method combined grid rotation and moving grid origin and gave fast, robust, and reproducible results and, in the majority of cases, achieved the minimum AOO. As well as determining minimum AOO, our method yielded a confidence interval that should be incorporated into existing tools that support species risk assessment. We recommend when recording AOO and other areal measurements that the methods; summary statistics across multiple iterations; angle and origin of the minimum grid; map projection; and datum be recorded, this will lead to more robust species risk assessments.

Aim Less than 6% of the worlds described plant species have been assessed on the IUCN Red List, leaving many species invisible to conservation prioritization. Large-scale Red List assessment of plant species is a challenge, as most species’ ranges have only been resolved to a coarse scale. As geographic distribution is a key assessment criterion on the IUCN Red List, we evaluate the use of coarse-scale distribution data in predictive models to assess the global scale and drivers of extinction risk in an economically important plant group, the bulbous monocotyledons. Location Global. Methods Using coarse-scale species distribution data, we train a machine learning model on biological and environmental variables for 148 species assessed on the IUCN Red List in order to identify correlates of extinction risk. We predict the extinction risk of 6439 ‘bulbous monocot’ species with the best of 13 models and map our predictions to identify potential hotspots of threat. Results Our model achieved 91% classification accuracy, with 88% of threatened species and 93% of non-threatened species accurately predicted. The model predicted 35% of bulbous monocots presently ‘Not Evaluated’ under IUCN criteria to be threatened and human impacts were a key correlate of threat. Spatial analysis identified some hotspots of threat where no bulbous monocots are yet on the IUCN Red List, for example central Chile. Main conclusions This is the first time a machine learning model has been used to determine extinction risk at a global scale in a species-rich plant group. As coarse-scale distribution data exist for many plant groups, our methods can be replicated to provide extinction risk predictions across the plant kingdom. Our approach can be used as a low-cost prioritization tool for targeting field-based assessments.

Extinction risk assessments are increasingly important to many stakeholders (Bennun et al. 2017) but there remain large gaps in our knowledge about the status of many species. The IUCN Red List of Threatened Species (IUCN 2019, hereafter Red List) is the most comprehensive assessment of extinction risk. However, it includes assessments of just 7% of all vascular plants, while 18% of all assessed animals lack sufficient data to assign a conservation status. The wide availability of species occurrence information through digitised natural history collections and aggregators such as the Global Biodiversity Information Facility (GBIF), coupled with machine learning methods, provides an opportunity to fill these gaps in our knowledge. Machine learning approaches have already been proposed to guide conservation assessment efforts (Nic Lughadha et al. 2018), assign a conservation status to species with insufficient data for a full assessment (Bland et al. 2014), and predict the number of threatened species across the world (Pelletier et al. 2018). The wide range in sources of species occurrence records can lead to data quality issues, such as missing, imprecise, or mistaken information. These data quality issues may be compounded in databases that aggregate information from multiple sources: many such records derive from field observations (78% for plant species in GBIF; Meyer et al. 2016) largely unsupported by voucher specimens that would allow confirmation or correction of their identification. Even where voucher specimens do exist, different taxonomic or geographic information can be held for a single collection event represented by duplicate specimens deposited in different natural history collections. Tools are available to help clean species occurrence data, but these cannot deal with problems like specimen misidentification, which previous work (Nic Lughadha et al. 2019) has shown to have a large impact on preliminary assessments of conservation status. Machine learning models based on species occurrence records have been reported to predict with high accuracy the conservation status of species. However, given the black-box nature of some of the better machine learning models, it is unclear how well these accuracies apply beyond the data on which the models were trained. Practices for training machine learning models differ between studies, but more interrogation of these models is required if we are to know how much to trust their predictions. To address these problems, we compare predictions made by a machine learning model when trained on specimen occurrence records that have benefitted from minimal or more thorough cleaning, with those based on records from an expert-curated database. We then explore different techniques to interrogate machine learning models and quantify the uncertainty in their predictions.

Citizen science networks and tools offer researchers innovative ways of gathering data which can be used for conservation purposes. By engaging multiple stakeholders (from non-specialist members of the public to professional researchers), harnessing the enthusiasm and goodwill of a global community of interested parties can enormously speed up the collection of data. Conservationists urgently need to accelerate the rate at which the extinction risk of species are assessed, ideally using International Union for Conservation of Nature (IUCN) Red List criteria. The palms are a group of plants with a particularly high risk of extinction, across the family, in the face of increasing habitat destruction and overexploitation of economically important taxa; as many as 83% of palm species in Madagascar are threatened with extinction. A global assessment of conservation status for the palm family was recognized as being urgently required nearly 20 years ago, but

The modifiable areal unit problem is prevalent across many aspects of spatial analysis within ecology and conservation. The problem is particularly manifested when calculating metrics for extinction risk estimation, for example, area of occupancy (AOO). Although embedded in the International Union for the Conservation of Nature (IUCN) Red List criteria, AOO is often not used or is poorly applied. We evaluated new and existing methods for calculating AOO from occurrence records and devised a method for determining the minimum AOO with a uniform grid. We evaluated the grid cell shape, origin, and rotation with real-world and simulated data and reviewed the effects on AOO values and possible impacts for species already assessed on the IUCN Red List. The AOO varied by up to 80%, and a ratio of cells to points of 1:1.21 yielded the maximum variation in the number of occupied cells. These findings potentially impact 3% of existing species on the IUCN Red List and species not yet assessed. Our new method combined grid rotation and moving grid origin and gave fast, robust, and reproducible results and, in the majority of cases, achieved the minimum AOO. As well as determining minimum AOO, our method yielded a confidence interval that should be incorporated into existing tools that support species risk assessment. We recommend when recording AOO and other areal measurements that the methods; summary statistics across multiple iterations; angle and origin of the minimum grid; map projection; and datum be recorded, this will lead to more robust species risk assessments.

The Global Strategy for Plant Conservation (GSPC) set an ambitious target to achieve a conservation assessment for all known plant species by 2020. We consolidated digitally available plant conservation assessments and reconciled their scientific names and assessment status to predefined standards to provide a quantitative measure of progress toward this target. The 241,919 plant conservation assessments generated represent 111,824 accepted land plant species (vascular plants and bryophytes, not algae). At least 73,081 and up to 90,321 species have been assessed at the global scale, representing 21-26% of known plant species. Of these plant species, at least 27,148 and up to 32,542 are threatened. Eighty plant families, including some of the largest, such as Asteraceae, Orchidaceae, and Rubiaceae, are underassessed and should be the focus of assessment effort if the GSPC target is to be met by 2020. Our data set is accessible online (ThreatSearch) and is a baseline that can be used to directly support other GSPC targets and plant conservation action. Although around one-quarter of a million plant assessments have been compiled, the majority of plants are still unassessed. The challenge now is to build on this progress and redouble efforts to document conservation status of unassessed plants to better inform conservation decisions and conserve the most threatened species.

Characterising a species’ geographical extent is central to many conservation assessments, including those of the IUCN Red List of threatened species. The IUCN recommends that extent of occurrence (EOO) to be quantified by drawing a minimum convex polygon (MCP) around known or inferred presence localities. EOO calculated from verified specimens is commonly used in Red List assessments when other data are scarce, as is the case for many threatened plant species. Yet rarely do these estimates incorporate inferred localities from species distribution models (SDMs). A key impediment stems from uncertainty about how SDM predictions relate to EOO. Here we address this issue by comparing the EOOs estimated from specimen localities with EOOs derived from SDMs for plant species occurring in Costa Rica and Panama. We first analyse 20 plant species, with well-known and well-sampled distributions, and train SDMs to subsamples of the data and assess how well the SDM-derived MCPs predict both the MCPs of the subsamples and the MCPs of the complete dataset. We find that when sample sizes are small (5 or 10 samples) the SDM-derived MCPs are actually closer to the complete dataset than to the MCPs of the subsamples, both in terms of EOO and geographically. This occurs when using a probability threshold based on maximum geographical similarity between the SDM-derived MCP and the subsample MCP; other threshold methods performed less well. For the species with less well-known distributions, the SDM-derived EOOs correlate strongly with, but tend to be larger than, EOOs estimated by point data. This implies that a SDM-derived EOO may be more representative of the full EOO than that drawn around known localities. Our findings reveal situations in which SDMs provide useful information that complements the IUCN Red Listing process.

GeoCAT is an open source, browser based tool that performs rapid geospatial analysis to ease the process of Red Listing taxa. Developed to utilise spatially referenced primary occurrence data, the analysis focuses on two aspects of the geographic range of a taxon: the extent of occurrence (EOO) and the area of occupancy (AOO). These metrics form part of the IUCN Red List categories and criteria and have often proved challenging to obtain in an accurate, consistent and repeatable way. Within a familiar Google Maps environment, GeoCAT users can quickly and easily combine data from multiple sources such as GBIF, Flickr and Scratchpads as well as user generated occurrence data. Analysis is done with the click of a button and is visualised instantly, providing an indication of the Red List threat rating, subject to meeting the full requirements of the criteria. Outputs including the results, data and parameters used for analysis are stored in a GeoCAT file that can be easily reloaded or shared with collaborators. GeoCAT is a first step toward automating the data handling process of Red List assessing and provides a valuable hub from which further developments and enhancements can be spawned.

The scale of the global biodiversity crisis means that international efforts to identify, conserve and monitor threatened species must be carried out at a greater speed than ever before. Recent developments in information technology present an opportunity to speed up the production of species conservation assessments, and methods and prospects for this are discussed in the context of the work being conducted on plant assessments for the International Union for Conservation of Nature (IUCN) Sampled Red List Index. The need for an internationally agreed upon, comparable, standardised system such as the Red List is emphasised here, but ultimately more efficient tech- niques must be developed to supplement the existing approach if this is to be able to meet the global demand.

The identification of species at risk of extinction is a central goal of conservation. As the use of data compiled for IUCN Red List assessments expands, a number of misconceptions regarding the purpose, application and use of the IUCN Red List categories and criteria have arisen. We outline five such classes of misconception; the most consequential drive proposals for adapted versions of the criteria, rendering assessments among species incomparable. A key challenge for the future will be to recognize the point where understanding has developed so markedly that it is time for the next generation of the Red List criteria. We do not believe we are there yet but, recognizing the need for scrutiny and continued development of Red Listing, conclude by suggesting areas where additional research could be valuable in improving the understanding of extinction risk among species.

The factors causing spatial variation in species richness remain poorly known. In this study, factors affecting species richness of palms (Palmae/Arecaceae) were studied along the elevational gradient of New Guinea. Interpolated elevational ranges were calculated from a database of all known collections for 145 species in 32 genera. The amount of land area at different elevations greatly affects the species richness gradient. If assessed in equal-elevation bands species richness appears to decline monotonically, but when assessed in equal-area bands species richness shows a pronounced mid-elevation peak, due to the large proportion of lowlands in New Guinea. By randomising species ranges within the total elevational gradient for palms and accounting for area, we found the mid-elevation peak to be consistent with a mid-domain effect caused by the upper and lower limits to palm distribution. Our study illustrates the importance of accounting for area in macroecological studies of richness gradients and introduces a novel yet simple method for doing this through the use of equal-area bands. Together, the effect of area and the mid-domain effect explain the majority of variation in species richness of New Guinea palms. We support calls for the multivariate assessment of the mid-domain effect on an equal footing with other potential explanations of species richness.

The establishment of baseline IUCN Red List assessments for plants is a crucial step in conservation planning. Nowhere is this more important than in biodiversity hotspots that are subject to significant anthropogenic pressures, such as Madagascar. Here, all Madagascar palm species are assessed using the IUCN Red List categories and criteria, version 3.1. Our results indicate that 83% of the 192 endemic species are threatened, nearly four times the proportion estimated for plants globally and exceeding estimates for all other comprehensively evaluated plant groups in Madagascar. Compared with a previous assessment in 1995, the number of Endangered and Critically Endangered species has substantially increased, due to the discovery of 28 new species since 1995, most of which are highly threatened. The conservation status of most species included in both the 1995 and the current assessments has not changed. Where change occurred, more species have moved to lower threat categories than to higher categories, because of improved knowledge of species and their distributions, rather than a decrease in extinction risk. However, some cases of genuine deterioration in conservation status were also identified. Palms in Madagascar are primarily threatened by habitat loss due to agriculture and biological resource use through direct exploitation or collateral damage. The recent extension of Madagascar's protected area network is highly beneficial for palms, substantially increasing the number of threatened species populations included within reserves. Notably, three of the eight most important protected areas for palms are newly designated. However, 28 threatened and data deficient species are not protected by the expanded network, including some Critically Endangered species. Moreover, many species occurring in protected areas are still threatened, indicating that threatening processes persist even in reserves. Definitive implementation of the new protected areas combined with local community engagement are essential for the survival of Madagascar's palms.

25 páginas, 8 figuras, 6 tablas., [EN] An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world’s ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity

Plant-diversity hotspots on a global scale are well established, but smaller local hotspots within these must be identified for effective conservation of plants at the global and local scales. We used the distributions of endemic and endemic-threatened species of Myrtaceae to indicate areas of plant diversity and conservation importance within the Atlantic coastal forests (Mata Atlântica) of Brazil. We applied 3 simple, inexpensive geographic information system (GIS) techniques to a herbarium specimen database: predictive species-distribution modeling (Maxent); complementarity analysis (DIVA-GIS); and mapping of herbarium specimen collection locations. We also considered collecting intensity, which is an inherent limitation of use of natural history records for biodiversity studies. Two separate areas of endemism were evident: the Serra do Mar mountain range from Parana to Rio de Janeiro and the coastal forests of northern Espirito Santo and southern Bahia. We identified 12 areas of approximately 35 km2 each as priority areas for conservation. These areas had the highest species richness and were highly threatened by urban and agricultural expansion. Observed species occurrences, species occurrences predicted from the model, and results of our complementarity analysis were congruent in identifying those areas with the most endemic species. These areas were then prioritized for conservation importance by comparing ecological data for each. Resumen: Los sitios de importancia para la diversidad de plantas estan identificados a escala global, pero se deben identificar sitios mas pequenos, locales, para la conservacion efectiva de plantas a escalas global y local. Utilizamos las distribuciones de especies endemicas y endemicas amenazadas de Myrtaceae para indicar areas de importancia para la diversidad y conservacion de plantas en los bosques de la costa del Atlantico (Mata Atlântica) de Brasil. Aplicamos tres tecnicas, simples y baratas, de sistemas de informacion geografica (SIG) a una base de datos de especimenes de herbario: modelado predictivo de la distribucion de especies (Maxent); analisis de complementariedad (DIVA-GIS) y mapeo de localidades de colecta de especimenes de herbario. Tambien consideramos la intensidad de colecta, que es una limitacion inherente al uso de registros de historia natural para estudios de biodiversidad. Dos areas de endemismo separadas fueron evidentes: la Serra do Mar de Parana a Rio de Janeiro y los bosques costeros del norte de Espirito Santo y el sur de Bahia. Identificamos 12 areas, aproximadamente de 35 km2 cada una, como sitios prioritarios para la conservacion. Estas areas tenian la mayor riqueza de especies y estaban muy amenazadas por la expansion urbana y agricola. Las ocurrencias observadas de especies, las ocurrencias de especies pronosticadas por el modelo y los resultados de nuestro analisis de complementariedad fueron congruentes en la identificacion de las areas con el mayor numero de especies endemicas. La importancia para la conservacion de estas areas fue priorizada posteriormente mediante la comparacion de datos ecologicos.

There is increased evidence that incorporating evolutionary history directly in conservation actions is beneficial, particularly given the likelihood that extinction is not random and that phylogenetic diversity (PD) is lost at higher rates than species diversity. This evidence is even more compelling in biodiversity hotspots, such as Madagascar, where less than 10% of the original vegetation remains. Here, we use the Leguminosae, an ecologically and economically important plant family, and a combination of phylogenetics and species distribution modelling, to assess biodiversity patterns and identify regions, coevolutionary processes and ecological factors that are important in shaping this diversity, especially during the Quaternary. We show evidence that species distribution and community PD are predicted by watershed boundaries, which enable the identification of a network of refugia and dispersal corridors that were perhaps important for maintaining community integrity during past climate change. Phylogenetically clustered communities are found in the southwest of the island at low elevation and share a suite of morphological characters (especially fruit morphology) indicative of coevolution with their main dispersers, the extinct and extant lemurs. Phylogenetically over-dispersed communities are found along the eastern coast at sea level and may have resulted from many independent dispersal events from the drier and more seasonal regions of Madagascar.

The IUCN Sampled Red List Index (SRLI) is a policy response by biodiversity scientists to the need to estimate trends in extinction risk of the world's diminishing biological diversity. Assessments of plant species for the SRLI project rely predominantly on herbarium specimen data from natural history collections, in the overwhelming absence of accurate population data or detailed distribution maps for the vast majority of plant species. This creates difficulties in re-assessing these species so as to measure genuine changes in conservation status, which must be observed under the same Red List criteria in order to be distinguished from an increase in the knowledge available for that species, and thus re-calculate the SRLI. However, the same specimen data identify precise localities where threatened species have previously been collected and can be used to model species ranges and to target fieldwork in order to test specimen-based range estimates and collect population data for SRLI plant species. Here, we outline a strategy for prioritizing fieldwork efforts in order to apply a wider range of IUCN Red List criteria to assessments of plant species, or any taxa with detailed locality or natural history specimen data, to produce a more robust estimation of the SRLI.