Chelsea D. Specht

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.

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.

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.

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.

Climate change is one of the most serious threats to global crops production at present and it will continue to be the largest threat in the future worldwide. Knowing how climate change affects crop productivity might help sustainability and crop improvement efforts. Under existing and projected climate change scenarios (2050s and 2070s in Ethiopia), the effect of global warming on the distribution of V. radiata and V. unguiculata was investigated. MaxEnt models were used to predict the current and future distribution pattern changes of these crops in Ethiopia using different climate change scenarios (i.e., lowest (RCP 2.6), moderate (RCP 4.5), and extreme (RCP 8.5)) for the years 2050s and 2070s. The study includes 81 and 68 occurrence points for V. radiata and V. unguiculata, respectively, along with 22 environmental variables. The suitability maps indicate that the Beneshangul Gumuz, Oromia, Amhara, SNNPR, and Tigray regions are the major Ethiopian regions with the potential to produce V. radiata, while Amhara, Gambella, Oromia, SNNPR, and Tigray are suitable for producing V. unguiculata. The model prediction for V. radiata habitat ranges distribution in Ethiopia indicated that 1.69%, 4.27%, 11.25% and 82.79% are estimated to be highly suitable, moderately suitable, less suitable, and unsuitable, respectively. On the other hand, the distribution of V. unguiculata is predicted to have 1.27%, 3.07%, 5.22%, and 90.44% habitat ranges that are highly suitable, moderately suitable, less suitable, and unsuitable, respectively, under the current climate change scenario by the year (2050s and 2070s) in Ethiopia. Among the environmental variables, precipitation of the wettest quarter (Bio16), solar radiation index (SRI), temperature seasonality (Bio4), and precipitation seasonality (Bio15) are discovered to be the most effective factors for defining habitat suitability for V. radiata, while precipitation of the wettest quarter (Bio16), temperature annual range (Bio7) and precipitation of the driest quarter (Bio17) found to be better habitat suitability indicator for V. unguiculata in Ethiopia. The result indicates that these variables were more relevant in predicting suitable habitat for these crops in Ethiopia. A future projection predicts that the suitable distribution region will become increasingly fragmented. In general, the study provides a scientific basis of suitable agro-ecological habitat for V. radiata and V. unguiculata for long-term crop management and production improvement in Ethiopia. Therefore, projections of current and future climate change impacts on such crops are vital to reduce the risk of crop failure and to identify the potential productive areas in the country.

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.

During the early to middle Eocene, a mid‐to‐high latitudinal position and enhanced hydrological cycle in Australia would have contributed to a wetter and “greener” Australian continent where today arid to semi‐arid climates dominate. Here, we revisit 12 southern Australian plant megafossil sites from the early to middle Eocene to generate temperature, precipitation and seasonality paleoclimate estimates, net primary productivity (NPP) and vegetation type, based on paleobotanical proxies and compare to early Eocene global climate models. Temperature reconstructions are uniformly subtropical (mean annual, summer, and winter mean temperatures 19–21 °C, 25–27 °C and 14–16 °C, respectively), indicating that southern Australia was ∼5 °C warmer than today, despite a >20° poleward shift from its modern geographic location. Precipitation was less homogeneous than temperature, with mean annual precipitation of ∼60 cm over inland sites and >100 cm over coastal sites. Precipitation may have been seasonal with the driest month receiving 2–7× less than mean monthly precipitation. Proxy‐model comparison is favorable with an 1680 ppm CO2 concentration. However, individual proxy reconstructions can disagree with models as well as with each other. In particular, seasonality reconstructions have systemic offsets. NPP estimates were higher than modern, implying a more homogenously “green” southern Australia in the early to middle Eocene, when this part of Australia was at 48–64 °S, and larger carbon fluxes to and from the Australian biosphere. The most similar modern vegetation type is modern‐day eastern Australian subtropical forest, although distance from coast and latitude may have led to vegetation heterogeneity.

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.

Abstract. Statistical climate reconstruction techniques are fundamental tools to study past climate variability from fossil proxy data. In particular, the methods based on probability density functions (or PDFs) can be used in various environments and with different climate proxies because they rely on elementary calibration data (i.e. modern geolocalised presence data). However, the difficulty of accessing and curating these calibration data and the complexity of interpreting probabilistic results have often limited their use in palaeoclimatological studies. Here, I introduce a new R package (crestr) to apply the PDF-based method CREST (Climate REconstruction SofTware) on diverse palaeoecological datasets and address these problems. crestr includes a globally curated calibration dataset for six common climate proxies (i.e. plants, beetles, chironomids, rodents, foraminifera, and dinoflagellate cysts) associated with an extensive range of climate variables (20 terrestrial and 19 marine variables) that enables its use in most terrestrial and marine environments. Private data collections can also be used instead of, or in combination with, the provided calibration dataset. The package includes a suite of graphical diagnostic tools to represent the data at each step of the reconstruction process and provide insights into the effect of the different modelling assumptions and external factors that underlie a reconstruction. With this R package, the CREST method can now be used in a scriptable environment and thus be more easily integrated with existing workflows. It is hoped that crestr will be used to produce the much-needed quantified climate reconstructions from the many regions where they are currently lacking, despite the availability of suitable fossil records. To support this development, the use of the package is illustrated with a step-by-step replication of a 790 000-year-long mean annual temperature reconstruction based on a pollen record from southeastern Africa.