Dan Henry Nicolson

The flowering phenology of many tropical mountain forest tree species remains poorly understood, including flowering synchrony and its drivers across neotropical ecosystems. We obtained herbarium records for 427 tree species from a long-term monitoring transect on the northwestern Ecuadorian Andes, sourced from the Global Biodiversity Information Facility and the Herbario Nacional del Ecuador. Using machine learning algorithms, we identified flowering phenophases from digitized specimen labels and applied circular statistics to build phenological calendars across six climatic regions within the neotropics. We found 47 939 herbarium records, of which 14 938 were classified as flowering by Random Forest Models. We constructed phenological calendars for six regions and 86 species with at least 20 flowering records. Phenological patterns varied considerably across regions, among species within regions, and within species across regions. There was limited interannual synchronicity in flowering patterns within regions primarily driven by bimodal species whose flowering peaks coincided with irradiance peaks. The predominantly high variability of phenological patterns among species and within species likely confers adaptative advantages by reducing interspecific competition during reproductive periods and promoting species coexistence in highly diverse regions with little or no seasonality.

Abstract After many decades of effective control of stem rust caused by the Puccinia graminis f.sp. tritici, (hereafter Pgt) the reported emergence of race TTKSK/Ug99 of Pgt in Uganda reignited concerns about epidemics worldwide because ∼90% of world wheat cultivars had no resistance to the new race. Since it was initially detected in Uganda in 1998, Ug99 variants have now been identified in thirteen countries in Africa and the Middle East. Stem rust has been a major problem in the past, and concern is increasing about the risk of return to Central and East Asia. Whilst control programs in North America and Europe relied on the use of resistant cultivars in combination with eradication of barberry (Berberis spp.), the alternate host required for the stem rust pathogen to complete its full lifecycle, the focus in East Asia was principally on the use of resistant wheat cultivars. Here, we investigate potential airborne transmission pathways for stem rust outbreaks in the Middle East to reach East Asia using an integrated modelling framework combining estimates of fungal spore deposition from an atmospheric dispersion model, environmental suitability for spore germination, and crop calendar information. We consider the role of mountain ranges in restricting transmission pathways, and we incorporate a representation of a generic barberry species into the lifecycle. We find viable transmission pathways to East Asia from the Middle East to the north via Central Asia and to the south via South Asia and that an initial infection in the Middle East could persist in East Asia for up to three years due to the presence of the alternate host. Our results indicate the need for further assessment of barberry species distributions in East Asia and appropriate methods for targeted surveillance and mitigation strategies should stem rust incidence increase in the Middle East region.

Saussurea is one of the largest and most rapidly evolving genera within the Asteraceae, comprising approximately 520 species from the Northern Hemisphere. A comprehensive infrageneric classification, supported by robust phylogenetic trees and corroborated by morphological and other data, has not yet been published. For the first time, we recovered a well-resolved nuclear phylogeny of Saussurea consisting of four main clades, which was also supported by morphological data. Our analyses show that ancient hybridization is the most likely source of deep cytoplasmic-nuclear conflict in Saussurea, and a phylogeny based on nuclear data is more suitable than one based on chloroplast data for exploring the infrageneric classification of Saussurea. Based on the nuclear phylogeny obtained and morphological characters, we proposed a revised infrageneric taxonomy of Saussurea, which includes four subgenera and 13 sections. Specifically, 1) S. sect. Cincta, S. sect. Gymnocline, S. sect. Lagurostemon, and S. sect. Strictae were moved from S. subg. Saussurea to S. subg. Amphilaena, 2) S. sect. Pseudoeriocoryne was moved from S. subg. Eriocoryne to S. subg. Amphilaena, and 3) S. sect. Laguranthera was moved from S. subg. Saussurea to S. subg. Theodorea.

The genus Rhodiola L., an integral part of traditional Chinese medicine and Tibetan medicine in China, exhibits a broad spectrum of applications. This genus contains key compounds such as ginsenosides, polysaccharides, and flavonoids, which possess anti-inflammatory, antioxidant, hypoglycaemic, immune-enhancing, and anti-hypoxic properties. As a vital raw material, Rhodiola L. contributes to twenty-four kinds of Chinese patent medicines and 481 health food products in China, finding extensive application in the health food sector. Recently, polysaccharides have emerged as a focal point in natural product research, with applications spanning the medicine, food, and materials sectors. Despite this, a comprehensive and systematic review of polysaccharides from the genus Rhodiola L. polysaccharides (TGRPs) is warranted. This study undertakes a systematic review of both domestic and international literature, assessing the research advancements and chemical functional values of polysaccharides derived from Rhodiola rosea. It involves the isolation, purification, and identification of a variety of homogeneous polysaccharides, followed by a detailed analysis of their chemical structures, pharmacological activities, and molecular mechanisms, structure-activity relationship (SAR) of TGRPs. The discussion includes the influence of molecular weight, monosaccharide composition, and glycosidic bonds on their biological activities, such as sulfation and carboxymethylation et al. Such analyses are crucial for deepening the understanding of Rhodiola rosea and for fostering the development and exploitation of TGRPs, offering a reference point for further investigations into TGRPs and their resource utilization.

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.

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.

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.

Predicting the likelihood that non-native species will be introduced into new areas remains one of conservation’s greatest challenges and, consequently, it is necessary to adopt adequate management measures to mitigate the effects of future biological invasions. At present, not much information is available on the areas in which non-native aquatic plant species could establish themselves in the Iberian Peninsula. Species distribution models were used to predict the potential invasion risk of (1) non-native aquatic plant species already established in the peninsula (32 species) and (2) those with the potential to invade the peninsula (40 species). The results revealed that the Iberian Peninsula contains a number of areas capable of hosting non-native aquatic plant species. Areas under anthropogenic pressure are at the greatest risk of invasion, and the variable most related to invasion risk is temperature. The results of this work were used to create the Invasion Risk Atlas for Alien Aquatic Plants in the Iberian Peninsula, a novel online resource that provides information about the potential distribution of non-native aquatic plant species. The atlas and this article are intended to serve as reference tools for the development of public policies, management regimes, and control strategies aimed at the prevention, mitigation, and eradication of non-native aquatic plant species.

The popularization of citizen-science platforms has increased the amount of data available in a fine spatial and temporal resolution, which can be used to fill distribution knowledge gaps through probabilistic maps. In this study, we gathered expert-based information and used species distribution models to produce two independent maps of the greater rhea ( Rhea americana , Rheiformes, Rheidae) distribution in the state of Rio Grande do Sul, Brazil. We integrated municipality level detection/non-detection data from five citizen-science datasets into a Bayesian site occupancy model, accounting for false negatives, sampling effort, habitat covariates, and spatial autocorrelation. We addressed whether habitat (grassland and crop field cover, number of rural properties) and spatial autocorrelation explains the realized occurrence of the species and compared model-based and expert-based occurrence maps. The mean estimated percentage of occupied municipalities was 48% (239 out of 497 municipalities), whereas experts declared 21% of the municipalities (103) as occupied by the species. While both mapping approaches showed greater rhea presence in most municipalities of the Pampa biome, they disagreed in the majority of the municipalities in the Atlantic Forest, where more fieldwork must be undertaken. The greater rhea distribution was exclusively explained by the spatial autocorrelation component, suggesting that the species expanded its distribution towards the north of the state, reaching the Atlantic Forest, following deforestation and agriculture expansion.

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.