Gwilym Lewis

While area-based approaches continue to dominate biodiversity conservation, there is growing recognition of the importance of the human dimensions of biodiversity. We applied the Important Plant Areas (IPA) approach in Colombia to identify key sites for the conservation of plant species with reported human uses. Drawing on the Checklist of Useful Plants of Colombia, we collated 1,045,889 clean occurrence records for 5400 native species from global data repositories and digitized herbaria. Through analysis based on regionalized grid cells, we identified 980 sites meeting IPA thresholds. These are primarily located in forest habitats, with only 19.8 % within existing national natural parks or internationally designated conservation areas. Grid cells were transformed to polygons based on overlapping ecosystems and administrative boundaries to form more meaningful site boundaries. A subsequent two-stage ranking procedure based on conservation value and richness found 46 sites to be of high priority, with 10 selected as top priorities for further investigation and conservation action. These 10 sites support significant populations of 33 threatened useful plant species and represent six of the 13 bioregions of Colombia in just 0.27 % of its land area. To progress from potential to confirmed IPAs, targeted fieldwork is required alongside stakeholder engagement and consultation, crucially involving local resource users. As a megadiverse country ranked second in the world for its botanical richness, effective IPA management would not only contribute to Colombian targets for sustainable development and conservation but would also support global targets to recover biodiversity for both planet and people.

Aim Fruit selection by animal dispersers with different mobility directly impacts plant geographical range size, which, in turn, may impact plant diversification. Here, we examine the interaction between fruit colour, range size and diversification rate in palms by testing two hypotheses: (1) species with fruit colours attractive to birds have larger range sizes due to high dispersal ability and (2) disperser mobility affects whether small or large range size has higher diversification, and intermediate range size is expected to lead to the highest diversification rate regardless of disperser. Location Global. Time Period Contemporary (or present). Major Taxa Studied Palms (Arecaceae). Methods Palm species were grouped based on likely animal disperser group for given fruit colours. Range sizes were estimated by constructing alpha convex hull polygons from distribution data. We examined disperser group, range size or an interaction of both as possible drivers of change in diversification rate over time in a likelihood dynamic model (Several Examined State-dependent Speciation and Extinction [SecSSE]). Models were fitted, rate estimates were retrieved and likelihoods were compared to those of appropriate null models. Results Species with fruit colours associated with mammal dispersal had larger ranges than those with colours associated with bird dispersal. The best fitting SecSSE models indicated that the examined traits were not the primary driver of the heterogeneity in diversification rates in the model. Extinction rate complexity had a marked impact on model performance and on diversification rates. Main Conclusions Two traits related to dispersal mobility, range size and fruit colour, were not identified as the main drivers of diversification in palms. Increased model extinction rate complexity led to better performing models, which indicates that net diversification should be estimated rather than speciation alone. However, increased complexity may lead to incorrect SecSSE model conclusions without careful consideration. Finally, we find palms with more mobile dispersers do not have larger range sizes, meaning other factors are more important determinants of range size.

Designing multispecies systems with suitable climatic affinity and identifying species' vulnerability under human‐driven climate change are current challenges to achieve successful adaptation of natural systems. To address this problem, we need to (1) identify groups of species with climatic similarity under climate scenarios and (2) identify areas with high conservation value under predicted climate change.To recognize species with similar climatic niche requirements that can be grouped for mixed cropping in Brazil, we employed ecological niche models (ENMs) and Spearman's ρ for overlap. We also used prioritization algorithms to map areas of high conservation value using two Shared Socioeconomic Pathways (SSP2‐4.5 and SSP5‐8.5) to assess mid‐term (2041–2060) and long‐term (2061–2080) climate change impacts.We identified 15 species groups with finer climatic affinities at different times depicted on hierarchical clustering dendrograms, which can be combined into agroecological agroforestry systems. Furthermore, we highlight the climatically suitable areas for these groups of species, thus providing an outlook of where different species will need to be planted over time to be conserved. In addition, we observed that climate change is predicted to modify the spatial association of these groups under different future climate scenarios, causing a mean negative change in species climatic similarity of 9.5% to 13.7% under SSP2‐4.5 scenario and 9.5% to 10.5% under SSP5‐8.5, for 2041–2060 and 2061–2080, respectively.Synthesis and applications. Our findings provide a framework for agroforestry conservation. The groups of species with finer climatic affinities identified and the climatically suitable areas can be combined into agroecological productive systems, and provide an outlook of where different species may be planted over time. In addition, the conservation priority zones displaying high climate stability for each species individually and all at once can be incorporated into Brazil's conservation plans by policymakers to prioritize specific sites. Lastly, we urge policymakers, conservation organizations and donors to promote interventions involving farmers and local communities, since the species' evaluated have proven to maintain landscapes with productive forest fragments and can be conserved in different Brazilian ecosystems.

Complex distribution data can be summarized by grouping species with similar or overlapping distributions to unravel spatial patterns and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data‐driven rigor of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices drive substantial variations in clustering outcomes and performance. To facilitate robust decision‐making, we provide guidance on choosing methods appropriate to one's study objective(s). As a case study, we apply our framework to modeled tree distributions in Borneo and subsequently evaluate the impact of land‐cover change on separate species groupings. Based on the modeled distribution of 390 tree species prior to anthropogenic land‐cover changes, we identified 11 distinct clusters that unraveled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associated species and elucidates the potential of resultant clusters as a spatially explicit framework for investigating distribution‐related questions in ecology, biogeography, and conservation. By adopting our methodological framework and publicly available codes, practitioners can leverage the ever‐growing abundance of distribution data to better understand complex spatial patterns among species distributions and the disparate effects of global changes on biodiversity.

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) – and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms – Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks – and compare these to two ethnobotanical selection approaches – based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best – attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

Global change effects on species are most pronounced when there is a large mismatch between past climate conditions, and the present climate, and this chasm will grow as global change proceeds without mitigation. Global change encompasses the alteration of temperature and precipitation patterns worldwide and these drivers can both increase the risk of species extirpation, and extinction. Juniperus phoenicea is an endemic plant species in the Mediterranean region of high conservation concern. Ensemble distribution models and the potential impact of future climate scenarios revealed that temperature, isothermality, and precipitation are the only significant bioclimatic factors affecting the geographical distribution of J. phoenicea. To study the potential impact of global change, we constrained the SDMs with a combination of two shared socio-economic pathways (SSPs) climate scenarios in the near (2030) and far (2090) future, together with two dispersal scenarios (full and limited). After removing incompatible regions based on current land-use distribution, the comparison of the current and future areas of occupancy revealed strong declines in the distribution of J. phoenicea. Applying the IUCN criteria, the species is predicted in all scenarios to be up-listed from the currently "least-concern" status to the "vulnerable", and potentially to the "critically endangered" status under the highest emission scenario in 2090. The range shifts predicted by our analysis draws attention to regions with stable distribution, and others predicted to become favorable for the species establishment. This information is essential for future conservation planning, including afforestation and reforestation programs.

The Caatinga biome is the largest dry tropical forest region in South America as well as one of the most vulnerable regions in the world to the climate changes forecast for this century. Climate forecasts for the biome include increased air temperature, reduced rainfall and aridization. This biome does not have a homogeneous landscape; instead it has several rainforest enclaves. This article describes a study to model the potential distribution of four epiphytic cactus species (Epiphyllum phyllanthus (L.) Haw., Rhipsalis floccosa Salm-Dyck ex Pfeiff., Rhipsalis lindbergiana K. Schum and Rhipsalis russellii Britton & Rose.) in the biome under future climate scenarios and traces out a prognosis for the enclaves and the biome. For that purpose, we used the MaxEnt modeling method, considering two future time intervals (2041-2060 and 2061-2080) and the interval 1961-1990 for the current situation, with the RCP4.5 and 8.5 scenarios. The projections for future potential distribution showed a spatial contractions greater than 88% found in the areas of high potential presence for the target species throughout the biome and in all the scenarios. The results strengthen the expectation of aridization in the Caatinga biome, with the loss or shrinkage of rainforest enclaves as time progresses.

The debated vegetation response to climate change can be investigated through palynological fossil records from past extreme climate conditions. In this context, the early Eocene (53.3 to 41.2 million years ago (Ma)) is often referred to as a model for a greenhouse Earth. In the Xining Basin, situated on the North-eastern Tibetan Plateau (NETP), this time interval is represented by an extensive and well-dated sedimentary sequence of evaporites and red mudstones. Here we focus on the palynological record of the Early Eocene Climatic Optimum (EECO; 53.3 to 49.1 Ma) and study the fossil gymnosperm pollen composition in these sediments. In addition, we also investigate the nearest living relatives (NLR) or botanical affinity of these genera and the paleobiogeographic implications of their occurrence in the Eocene of the NETP. To reach our objective, we complemented transmitted light microscopy with laser scanning- and electron microscopy techniques, to produce high-resolution images, and illustrate the morphological variation within fossil and extant gymnosperm pollen. Furthermore, a morphometric analysis was carried out to investigate the infra- and intrageneric variation of these and related taxa. To place the data in context we produced paleobiogeographic maps for Phyllocladidites and for other Podocarpaceae, based on data from a global fossil pollen data base, and compare these with modern records from GBIF. We also assessed the climatic envelope of the NLR. Our analyses confirm the presence of Phyllocladidites (NLR Phyllocladus, Podocarpaceae) and Podocarpidites (NLR Podocarpus, Podocarpaceae) in the EECO deposits in the Xining Basin. In addition, a comparative study based on literature suggests that Parcisporites is likely a younger synonym of Phyllocladidites. Our findings further suggest that the Phyllocladidites specimens are derived from a lineage that was much more diverse than previously thought, and which had a much larger biogeographical distribution during the EECO than at present. Based on the climatic envelope of the NLR, we suggest that the paleoclimatic conditions in the Xining Basin were warmer and more humid during the EECO. We conclude that phylloclade-type conifers typical of the southern-temperate podocarp forests, had a northward geographical expansion during the EECO, followed by extirpation.

Leucaena leucocephala , previously known as ‘miracle tree’ because of its numerous agroforestry uses has become a serious invasive species in tropical regions. Despite the risks associated with the spread of L. leucocephala , changes in its distribution with respect to climate are poorly understood, particularly in Africa where it has been widely introduced in more than 30 countries. To provide first-line information for the management of L. leucocephala , we examined its potential distribution in Africa using ecological niche modelling. We identified bioclimatic variables that determine the global distribution of L. leucocephala , and calibrated niche models using MaxEnt and species occurrences recorded between 1973 and 2013. The potential distribution of this species was estimated from model projections in Africa based on current and future climatic conditions. We tested the hypothesis of niche conservatism for L. leucocephala by comparing its climatic niche in Africa to that of its native range. Under current conditions, L. leucocephala is constrained between 30° S and 20° N in Africa, with the broadest distribution in East Africa. High rainfall areas in Central Africa with no known records of this species hitherto, were found to be highly suitable for its establishment. We predicted a significant decrement in the extent of areas at risk of invasion by L. Leucocephala under changing climates in Africa. Our results revealed that the study species occupies a similar but non-identical climatic niche in Africa in relation to its native niche. Climate change is likely to impede the spread of L. leucocephala in Africa.

Mesoamerican mountains are important centers of endemism and diversity of epiphytes. The Sierra Madre of Chiapas in southeastern Mexico is a mountainous region of great ecological interest due to its high biological richness. We present the first checklist of epiphytes for this region based on a compilation of various information sources. In addition, we determined the conservation status for each species based on the Mexican Official Standard (NOM-059-SEMARNAT-2010), endemism based on geopolitical boundaries, spatial completeness with inventory completeness index, richness distribution with range maps, and the relationship between climatic variables (temperature and rainfall) with species richness using generalized additive models. Our dataset includes 9,799 records collected between 1896-2017. Our checklist includes 708 epiphytes within 160 genera and 26 families; the most species-rich family was Orchidaceae (355 species), followed by Bromeliaceae (82) and Polypodiaceae (79). There were 74 species within a category of risk and 59 species considered endemic. Completeness of epiphyte richness suggests that sampling is still largely incomplete, particularly in the lower parts of the mountain system. Species and family range maps show the highest richness at high elevations, while geographically richness increases towards the southeast. Epiphyte richness increases with increased rainfall, although a unimodal pattern was observed along the temperature gradient with a species richness peak between 16-20 C°. The Sierra Madre of Chiapas forms a refuge to more than 40% of all epiphytes reported for Mexico and its existing network of protected areas overlaps with the greatest epiphyte richness.