Isaac Henry Burkill

AbstractPremiseThe ecological conditions that constrain plants to an environmental niche are assumed to be constant through time. While the fossil record has been used previously to test for niche conservatism of woody flowering plants, additional studies are needed in other plant groups especially since they can provide insight with paleoclimatic reconstructions, high biodiversity in modern terrestrial ecosystems, and significant contributions to agriculture.MethodsWe tested climatic niche conservatism across time by characterizing the climatic niches of living herbaceous ginger plants (Zingiberaceae) and woody dawn redwood (Metasequoia) against paleoniches reconstructed based on fossil distribution data and paleoclimatic models.ResultsDespite few fossil Zingiberaceae occurrences in the latitudinal tropics, unlike living Zingiberaceae, extinct Zingiberaceae likely experienced paratropical conditions in the higher latitudes, especially in the Cretaceous and Paleogene. The living and fossil distributions of Metasequoia largely remain in the upper latitudes of the northern hemisphere. The Zingiberaceae shifted from an initial subtropical climatic paleoniche in the Cretaceous, toward a temperate regime in the late Cenozoic; Metasequoia occupied a more consistent climatic niche over the same time intervals.ConclusionsBecause of the inconsistent climatic niches of Zingiberaceae over geologic time, we are less confident of using them for taxonomic‐based paleoclimatic reconstruction methods like nearest living relative, which assume a consistent climatic niche between extant and extinct relatives; we argue that the consistent climatic niche of Metasequoia is more appropriate for these reconstructions. Niche conservatism cannot be assumed between extant and extinct plants and should be tested further in groups used for paleoclimatic reconstructions.

ABSTRACTAimFerns are globally distributed, yet the number of studies examining the historical evolution of African taxa is relatively low. Investigation of the evolution of African fern diversity is critical in order to understand patterns and processes that have global relevance (e.g., the pantropical diversity disparity [PDD] pattern). This study aims to examine when and from where a globally distributed fern lineage arrived in sub‐Saharan Africa, to obtain a better understanding of potential processes contributing to patterns of diversity across the region.LocationGlobal, sub‐Saharan Africa.TaxonAsplenium (Aspleniaceae).MethodsWe analysed five loci from 537 Asplenium taxa using a maximum likelihood (IQ‐Tree) phylogenetic framework. For age estimation, we performed penalised likelihood as implemented in treePL, and executed a Bayesian analysis using BEAST. Biogeographical analyses were carried out using BioGeoBEARS.ResultsMost dispersals into Africa occurred within the last ~55 myr, with the highest diversity of sub‐Saharan African taxa concentrated in two clades, each of which descended from an Asian ancestor. Additional dispersals to sub‐Saharan Africa can be found throughout the phylogeny. Lastly, potential cryptic species diversity exists within Asplenium as evidenced by several polyphyletic taxa.Main ConclusionsWe recover multiple dispersals of Asplenium to sub‐Saharan Africa, with two major lineages likely diversifying after arrival.

Aim Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses.InnovationWe introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed.Main conclusionsoccTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

Present-day geographic and phylogenetic patterns often reflect the geological and climatic history of the planet. Neontological distribution data are often sufficient to unravel a lineage’s biogeographic history, yet ancestral range inferences can be at odds with fossil evidence. Here, I use the fossilized birth–death process and the dispersal–extinction cladogenesis model to jointly infer the dated phylogeny and range evolution of the tree fern order Cyatheales. I use data for 101 fossil and 442 extant tree ferns to reconstruct the biogeographic history of the group over the last 220 million years. Fossil-aware reconstructions evince a prolonged occupancy of Laurasia over the Triassic–Cretaceous by Cyathealean tree ferns, which is evident in the fossil record but hidden from analyses relying on neontological data alone. Nonetheless, fossil-aware reconstructions are affected by uncertainty in fossils’ phylogenetic placement, taphonomic biases, and specimen sampling and are sensitive to interpretation of paleodistributions and how these are scored. The present results highlight the need and challenges of incorporating fossils into joint inferences of phylogeny and biogeography to improve the reliability of ancestral geographic range estimation.

Since 1997 Tanzania has undertaken a process to identify and declare a network of Nature Forest Reserves (NFRs) with high biodiversity values, from within its existing portfolio of national Forest Reserves, with 16 new NFRs declared since 2015. The current network of 22 gazetted NFRs covered 948,871 hectares in 2023. NFRs now cover a range of Tanzanian habitat types, including all main forest types—wet, seasonal, and dry—as well as wetlands and grasslands. NFRs contain at least 178 of Tanzania’s 242 endemic vertebrate species, of which at least 50% are threatened with extinction, and 553 Tanzanian endemic plant taxa (species, subspecies, and varieties), of which at least 50% are threatened. NFRs also support 41 single-site endemic vertebrate species and 76 single-site endemic plant taxa. Time series analysis of management effectiveness tracking tool (METT) data shows that NFR management effectiveness is increasing, especially where donor funds have been available. Improved management and investment have resulted in measurable reductions of some critical threats in NFRs. Still, ongoing challenges remain to fully contain issues of illegal logging, charcoal production, firewood, pole-cutting, illegal hunting and snaring of birds and mammals, fire, wildlife trade, and the unpredictable impacts of climate change. Increased tourism, diversified revenue generation and investment schemes, involving communities in management, and stepping up control measures for remaining threats are all required to create a network of economically self-sustaining NFRs able to conserve critical biodiversity values.

The information on ecological niches of the Marula tree, Sclerocarya birrea (A. Rich.) Horchst. subspecies are needed for sustainable management of this tree, considering its nutritional, economic, and ecological benefits. However, despite Tanzania being regarded as a global genetic center of diversity of S. birrea, information on the subspecies ecological niches is lacking. We aimed to model ecological niches of S. birrea subspecies in Tanzania under the current and future climates. Ecological niches under the current climate were modelled by using ecological niche models in MaxEnt using climatic, edaphic, and topographical variables, and subspecies occurrence data. The Hadley Climate Center and National Center for Atmospheric Research's Earth System Models were used to predict ecological niches under the medium and high greenhouse gases emission scenarios for the years 2050 and 2080. Area under the curves (AUCs) were used to assess the accuracy of the models. The results show that the models were robust, with AUCs of 0.85–0.95. Annual and seasonal precipitation, elevation, and soil cation exchange capacity are the key environmental factors that define the ecological niches of the S. birrea subspecies. Ecological niches of subsp. caffra, multifoliata, and birrea are currently found in 30, 22, and 21 regions, and occupy 184 814 km2, 139 918 km2, and 28 446 km2 of Tanzania's land area respectively, which will contract by 0.4–44% due to climate change. Currently, 31–51% of ecological niches are under Tanzania’s protected areas network. The findings are important in guiding the development of conservation and domestication strategies for the S. birrea subspecies in Tanzania.

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

The threat of invasive species to biodiversity and ecosystem structure is exacerbated by the increasingly concerning outlook of predicted climate change and other human influences. Developing preventative management strategies for invasive plant species before they establish is crucial for effective management. To examine how climate change may impact habitat suitability, we modeled the current and future habitat suitability of two terrestrial species, Geranium lucidum and Pilosella officinarum , and two aquatic species, Butomus umbellatus and Pontederia crassipes , that are relatively new invasive plant species regionally, and are currently spreading in the Pacific Northwest (PNW, North America), an area of unique natural areas, vibrant economic activity, and increasing human population. Using North American presence records, downscaled climate variables, and human influence data, we developed an ensemble model of six algorithms to predict the potential habitat suitability under current conditions and projected climate scenarios RCP 4.5, 7.0, and 8.5 for 2050 and 2080. One terrestrial species ( P. officinarum ) showed declining habitat suitability in future climate scenarios (contracted distribution), while the other terrestrial species ( G. lucidum ) showed increased suitability over much of the region (expanded distribution overall). The two aquatic species were predicted to have only moderately increased suitability, suggesting aquatic plant species may be less impacted by climate change. Our research provides a template for regional-scale modelling of invasive species of concern, thus assisting local land managers and practitioners to inform current and future management strategies and to prioritize limited available resources for species with expanding ranges.

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.

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.