John Medley Wood

Aim The aim of this study was to further advance our understanding of the species-rich, and ecologically important angiosperm family Combretaceae to provide new insights into their evolutionary history. We assessed phylogenetic relationships in the family using target capture data and produced a dated phylogenetic tree to assess fruit dispersal modes and patterns of distribution. Location Tropical and subtropical regions. Time Period Cretaceous to present. Major Taxa Studied Family Combretaceae is a member of the rosid clade and comprises 10 genera and more than 500 species, predominantly assigned to genera Combretum and Terminalia, and occurring on all continents and in a wide range of ecosystems. Methods We use a target capture approach and the Angiosperms353 universal probes to reconstruct a robust dated phylogenetic tree for the family. This phylogenetic framework, combined with seed dispersal traits, biome data and biogeographic ranges, allows the reconstruction of the biogeographical history of the group. Results Ancestral range reconstructions suggest a Gondwanan origin (Africa/South America), with several intercontinental dispersals within the family and few transitions between biomes. Relative abundance of fruit dispersal types differed by both continent and biome. However, intercontinental colonizations were only significantly enhanced by water dispersal (drift fruit), and there was no evidence that seed dispersal modes influenced biome shifts. Main Conclusions Our analysis reveals a paradox as drift fruit greatly enhanced dispersal distances at intercontinental scale but did not affect the strong biome conservatism observed.

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.

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.

Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.

Striga asiatica (dicot), an obligate hemiparasitic of monocots, is a potential threat to South America. Determining the ecological factors that explain the occurrence and predicting suitable areas for S. asiatica are fundamental for designing prevention strategies. We developed a Spatio-temporal dynamics model and evaluated Brazil's Weekly Growth Index (GIW) for S. asiatica. We analyzed four Brazilian regions (Central-West, South, Southeast, and Northeast) to verify the local seasonal variation of the species in climatic data. Our results indicated areas with favorable climatic suitability for the species in part of South America. Seasonal assessment models showed that high rainfall and the dry and cold periods common in tropical regions affect the GIW for S. asiatica. When we associate periods with maximum rainfall of 53 mm per week and temperature above 20 °C, the GIW approaches the optimal index for the regions evaluated, indicating the influence of soil moisture and air temperature. Our risk assessment indicated that the Southeast and Northeast are at the most significant risk of S. asiatica invasion. Projections for climate change between 2040–2059 showed expansions in areas suitable for S. asiatica compared to the current climate of South America.

CO2‐fertilisation is implicated in the widespread and significant woody encroachment of savannas due to CO2‐stimulated increases in belowground reserves that enhance sapling regrowth after fire. However, the effect of CO2 concentration ([CO2]) on tree responses to the other major disturbance in savannas, herbivory, is poorly understood. Herbivory‐responses cannot be predicted from fire‐responses, as herbivore effects occur earlier during establishment and are moderated by plant palatability and defence rather than belowground carbon accumulation.

Striga asiatica (Lamiales: Orobanchaceae), a hemi-parasitic plant native to sub-Saharan Africa and tropical Asia, is particularly problematic to rice, corn, and sorghum cultivation in Africa. Striga asiatica produces a large number of small sized (<0.5 mm) seeds, thereby facilitating easy dispersion by commercial exchange of contaminated grains. The distribution of this species in Africa is regulated by climate, which is the main factor determining local suitability. Modelling is a useful tool to analyse climate suitability for species. This study aimed to determine the areas more vulnerable to S. asiatica invasion both in the present and under the projected climate change model using two methods: MaxEnt (as a correlative approach) and CLIMEX (as a semi-mechanistic approach). The MIROC-H Global Climate Model and the A2 and RCP 8.5 scenarios (the most pessimistic one) were used. Our projections indicated areas suitable for S. asiatica invasion in all continents under both present and projected climate change, with high suitability areas in South America, Africa, and Europe. We found agreement and disagreement between CLIMEX and MaxEnt outputs and the extent of disagreement on the increases in climate suitability by 2050 and 2100 in North America, Europe, and eastern, southern, and western Australia. This study provides a useful tool to design strategies aimed at preventing the introduction and establishment of S. asiatica in South America, with considerable agreement between CLIMEX and MaxEnt outputs.

Aim The relationships between biome shifts and global environmental changes in temperate zone habitats have been extensively explored; yet, the historical dynamics of taxa found in the tropical rain forest (TRF) remain poorly known. This study aims to reconstruct the relationships between tropical rain forest shifts and global environmental changes through the patterns of historical biogeography of a pantropical family of monocots, the Zingiberaceae. Location Global. Taxon Zingiberaceae. Methods We sampled DNA sequences (nrITS, trnK, trnL-trnF and psbA-trnH) from GenBank for 77% of the genera, including 30% of species, in the Zingiberaceae. Global fossil records of the Zingiberaceae were collected from literatures. Rates of speciation, extinction and diversification were estimated based on phylogenetic data and fossil records through methods implemented in BAMM. Ancestral ranges were estimated using single-tree BioGeoBEARS and multiple-trees BioGeoBEARS in RASP. Dispersal rate through time and dispersal rate among regions were calculated in R based on the result of ancestral estimation. Results The common ancestor of the Zingiberaceae likely originated in northern Africa during the mid-Cretaceous, with later dispersal to the Asian tropics. Indo-Burma, rather than Malesia, was likely a provenance of the common ancestor of Alpinioideae–Zingiberoideae. Several abrupt shifts of evolutionary rates from the Palaeocene were synchronized with sudden global environmental changes. Main conclusions Integrating phylogenetic patterns with fossil records suggests that the Zingiberaceae dispersed to Asia through drift of the Indian Plate from Africa in the late Palaeocene. Formation of island chains, land corridors and warming temperatures facilitated the emigration of the Zingiberaceae to a broad distribution across the tropics. Moreover, dramatic fluctuations of the speciation rate of Zingiberoideae appear to have been synchronized with global climate fluctuations. In general, the evolutionary history of the Zingiberaceae broadens our understanding of the association between TRF shifts in distribution and past global environmental changes, especially the origin of TRF in Southeast Asia.

The early Eocene (∼56‐48 million years ago) is characterised by high CO2 estimates (1200‐2500 ppmv) and elevated global temperatures (∼10 to 16°C higher than modern). However, the response of the hydrological cycle during the early Eocene is poorly constrained, especially in regions with sparse data coverage (e.g. Africa). Here we present a study of African hydroclimate during the early Eocene, as simulated by an ensemble of state‐of‐the‐art climate models in the Deep‐time Model Intercomparison Project (DeepMIP). A comparison between the DeepMIP pre‐industrial simulations and modern observations suggests that model biases are model‐ and geographically dependent, however these biases are reduced in the model ensemble mean. A comparison between the Eocene simulations and the pre‐industrial suggests that there is no obvious wetting or drying trend as the CO2 increases. The results suggest that changes to the land sea mask (relative to modern) in the models may be responsible for the simulated increases in precipitation to the north of Eocene Africa. There is an increase in precipitation over equatorial and West Africa and associated drying over northern Africa as CO2 rises. There are also important dynamical changes, with evidence that anticyclonic low‐level circulation is replaced by increased south‐westerly flow at high CO2 levels. Lastly, a model‐data comparison using newly‐compiled quantitative climate estimates from palaeobotanical proxy data suggests a marginally better fit with the reconstructions at lower levels of CO2.

For many regions, such as in South America, it is unclear how well the existent protected areas network (PAs) covers different taxonomic groups and if there is a coverage bias of PAs towards certain biomes or species. Publicly available occurrence data along with ecological niche models might help to overcome this gap and to quantify the coverage of taxa by PAs ensuring an unbiased distribution of conservation effort. Here, we use an occurrence database of 271 species from the cacao family (Malvaceae) to address how South American PAs cover species with different distribution, abundance, and threat status. Furthermore, we compared the performance of online databases, expert knowledge, and modelled species distributions in estimating species coverage in PAs. We found 79 species from our survey (29% of the total) lack any record inside South American PAs and that 20 out of 23 species potentially threatened with extinction are not covered by PAs. The area covered by South American PAs was low across biomes, except for Amazonia, which had a relative high PA coverage, but little information on species distribution within PA available. Also, raw geo-referenced occurrence data were underestimating the number of species in PAs, and projections from ecological niche models were more prone to overestimating the number of species represented within PAs. We discuss that the protection of South American flora in heterogeneous environments demand for specific strategies tailored to particular biomes, including making new collections inside PAs in less collected areas, and the delimitation of more areas for protection in more known areas. Also, by presenting biasing scenarios of collection effort in a representative plant group, our results can benefit policy makers in conserving different spots of tropical environments highly biodiverse.