Hazel Marguerite Schmoll

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.

Pinyon–juniper (PJ) woodlands are among the most widespread ecosystems in rangelands of western North America, supporting diverse wildlife habitat, recreation, grazing, and cultural/spiritual enrichment. Anticipating future distribution shifts under changing climate will be critical to climate adaptation and conservation efforts in these ecosystems. Here, we evaluate drivers of PJ tree species’ distributions and project changes in response to future climate change. We developed species distribution models with dryland-focused predictors to project environmental suitability changes across the entirety of three pinyon and six juniper species ranges. We identify areas of robust suitability change by combining suitability projections from multiple emissions scenarios and time periods. PJ species’ suitabilities respond to many temperature and moisture covariates expected to change in the future. Projected responses among PJ species are highly variable, ranging from modest declines with concurrent gains for overall little net change to wide-ranging declines with no gains for overall range contractions. Environmental suitability is projected to decline broadly across the arid United States Southwest and remain relatively stable across the northern Great Basin and Colorado Plateau. Our results suggest unique responses of PJ species to future climate change. We found that species were projected to experience more losses than gains in suitability, for overall range shrinks rather than shifts. Land managers have the capacity to increase woodland resilience to drought, and our results can inform rangeland-wide management planning and conservation efforts in PJ woodlands.

Nonnative invasive plants (weeds) negatively impact native ecosystems, and their effects are likely to increase with continuing global trade. Biological weed control has been employed as a cost-effective and sustainable management option for weeds in the USA since 1902. Biological control programs require careful prioritization of target weeds to ensure the most appropriate targets are selected to obtain the greatest beneficial outcomes with available resources. The Biological Control Target Selection (BCTS) system was developed by researchers in South Africa as an objective, transparent approach to prioritizing new weed biological control targets. The BCTS system was recently modified and applied to 295 state-regulated weeds in the western USA for which no biological control agents have yet been released. This paper presents the results of that application, identifying the most suitable candidates for new biological control programs as well as problematic weeds for which the likelihood of successful biological control is low.Top-ranked species in the western USA are biennial or perennial weeds that occur in stable habitats, are established in more than one state, have traits deemed difficult to control with conventional methods, have large negative impacts and no conflicts of interest outside of the horticultural industry, and have substantial information available on potential biocontrol agents. Fifteen of the 20 top-ranked species are already targets of ongoing biological control programs in the USA. When species with current programs are excluded from the analysis, the next 20 top-ranked species largely differ by having less information available on potential biological control agents and having native or economically important congeners in the USA. Results from this framework provide valuable insights to the prioritization of current and future biocontrol research programs in the western USA.

Non‐native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well‐timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range‐wide phenology forecasts for two problematic invasive annual grasses: cheatgrass (Bromus tectorum), and red brome (Bromus rubens). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera imagery, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photothermal time models that had been adjusted for topography using gridded continuous heat‐insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management actions such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients. These phenology models also have the potential to be operationalized for within‐season or within‐year decision support.

The Khanka Lowland forest-steppe is the most eastern outpost of the Eurasian steppe biome. It includes unique grassland plant communities with rare steppe species. These coenosis have changed under the influence of anthropogenic activity, especially during the last 100 years and included both typical steppe species and nemoral mesophytic species. To distinguish these ecological groups of plants the random forest method with three datasets of environmental variables was applied. Specifically, a model of classification with the most important bioindices to predict a mesophytic ecological group of plants with a sensitivity greater than 80% was constructed. The data demonstrated the presence of steppe species that arrived at different times in the Primorye Territory. Most of these species are associated with the Mongolian-Daurian relict steppe complex and habit in the Khanka Lowland. Other species occur only in mountains in Primorye Territory and do not persist in the Khanka Lowland. These findings emphasize the presence of relict steppe communities with a complex of true steppe species in the Khanka Lowland. Steppe communities exhibit features of anthropogenic influence definitely through the long land use period but are not anthropogenic in origin. The most steppe species are located at the eastern border of distribution in the Khanka Lowlands and are valuable in terms of conservation and sources of information about steppe species origin and the emergence of the steppe biome as a whole.

Premise Vertical surfaces in urban environments represent a potential expansion of niche space for lithophytic fern species. There are, however, few records of differential success rates of fern species in urban environments.MethodsThe occurrence rates of 16 lithophytic fern species native to the northeastern USA in 14 biomes, including four urban environments differentiated by percentage of impervious surfaces, were evaluated. In addition, the natural macroclimatic ranges of these species were analyzed to test whether significant differences existed in climatic tolerance between species that occur in urban environments and species that do not.ResultsThree species appear to preferentially occur in urban environments, two species may facultatively occur in urban environments, and the remaining 11 species preferentially occur in nondeveloped rural environments. The natural range of fern species that occur in urban environments had higher summer temperatures than the range of species that do not, whereas other macroclimatic variables, notably winter temperatures and precipitation, were less important or insignificant.ConclusionsVertical surfaces in urban environments may represent novel niche space for some native lithophytic fern species in northeastern USA. However, success in this environment depends, in part, on tolerance of the urban heat island effect, especially heating of impervious surfaces in summer.

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.

Species occurrence-based climate niche models (CNMs) serve as valuable tools for predicting the future ranges of species’ suitable habitats, aiding the development of climate change adaptation strategies. However, these models do not address an essential aspect - productivity, which holds economic significance for timber production and ecological importance for carbon sequestration and ecosystem services. In this study, we investigated the potential to extend the CNMs to predict species productivity under various climate conditions. Lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.) were selected as our model species due to their comprehensive range-wide occurrence data and measurement of site productivity. To achieve this, we compared and optimized the performance of four individual modeling algorithms (Random Forest (RF), Maxent, Generalized Boosted Models (GBM), and Generalized Additive Model (GAM)) in reflecting site productivity by evaluating the effect of spatial filtering, and the ratio of presence to absence (p/a ratio) observations. Additionally, we applied a binning process to capture the overarching trend of climatic effects while minimizing the impact of other factors. We observed consistency in optimal performance across both species when using the unfiltered data and a 1:1.5 p/a ratio, which could potentially be extended to other species. Among the modeling algorithms explored, we selected the ensemble model combining RF and Maxent as the final model to predict the range-wide site productivity for both species. The predicted range-wide site productivity was validated with an independent dataset for each species and yielded promising results (R2 above 0.7), affirming our model’s credibility. Our model introduced an innovative approach for predicting species productivity with high accuracy using only species occurrence data, and significantly advanced the application of CNMs. It provided crucial tools and insights for evaluating climate change's impact on productivity and holds a better potential for informed forest management and conservation decisions.

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.

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.