Josef Murr

Parasitic weeds in the genera Orobanche, Phelipanche (broomrapes) and Striga (witchweeds) have a devastating impact on food security across much of Africa, Asia and the Mediterranean Basin. Yet, how climatic factors might affect the range expansion of these weeds in the context of global environmental change remains unexplored. We examined satellite‐based environmental variables such as surface temperature, root zone soil moisture, and elevation, in relation to parasitic weed distribution and environmental conditions over time, in combination with observational data from the Global Biodiversity Information Facility (GBIF). Our analysis reveals contrasting environmental and altitude preferences in the genera Striga and Orobanche. Asiatic witchweed (Striga asiatica), which infests corn, rice, sorghum, and sugar cane crops, appears to be expanding its range in high elevation habitats. It also shows a significant association with heat‐moisture coupling events, the frequency of which is rising in such environments. These results point to geographical shifts in distribution and abundance in parasitic weeds due to climate change.

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.

Crop Wild Relatives (CWR) are wild plant taxa genetically close to a crop. Being a precious source of genetic variability and of traits for crop improvement, CWR have a high socio-economic value and are identified among the main plant genetic resources. Alarming enough, the inter- and intraspecific diversity, as well as their habitat diversity, is under threat of irremediable loss. Italy is the second richest country in Europe in terms of plant species number; applying the taxon group concept 5712 have been recently identified as CWR. The aims of the present research are to identify the best sites for: i) the institution of genetic reserves to actively protect CWR species of the key crop genera as Allium, Brassica and Triticum and ii) performing new collection missions to reach adequate ex situ conservation of target species. Georeferenced data were retrieved from different online databases. CAPFITOGEN tools were initially used to develop an ecogeographic land characterisation map (ELC) of Italy. Geographical distribution data were assembled for 379 populations of 18 CWR taxa. Results of the complementarity analysis showed that 10 protected areas provide coverage of the 46.4% of the target conservation units and include 66.7% of the priority CWR taxa investigated. Alarming enough, only 7.4% of the 379 populations are currently conserved ex situ; among the 18 ecogeographic land characterisation categories only 3 are covered by ex situ conservation. This is the first study where most suitable protected areas for the institution of genetic reserves are proposed for Italy for the protection of multiple CWR taxa of key genera; this is relevant also considering the global value of many of the related crop such as different wheat species, cabbages, rape, garlic and onion. Being already dedicated to habitat and species conservation, the identified sites are optimal candidates for the institution of genetic reserves. Results will hopefully also guide new collecting missions that are urgently needed to strength ex situ conservation of such precious genetic resources.

Camelina [Camelina sativa (L.) Crantz] is a Brassicaceae oilseed that is gaining interest worldwide as low‐maintenance crop for diverse biobased applications. One of the most important factors determining its productivity is climate. We conducted a bioclimate analysis in order to analyze the relationship between climatic factors and the productivity of spring‐type camelina seeded in the spring, and to identify regions of the world with potential for camelina in this scenario. Using the modelling tool CLIMEX, a bioclimatic model was developed for spring‐seeded spring‐type camelina to match distribution, reported seed yields and phenology records in North America. Distribution, yield, and phenology data from outside of North America were used as independent datasets for model validation and demonstrated that model projections agreed with published distribution records, reported spring‐seeded camelina yields, and closely predicted crop phenology in Europe, South America, and Asia. Sensitivity analysis, used to quantify the response of camelina to changes in precipitation and temperature, indicated that crop performance was more sensitive to moisture than temperature index parameters, suggesting that the yield potential of spring‐seeded camelina may be more strongly impacted by water‐limited conditions than by high temperatures. Incremental climate scenarios also revealed that spring‐seeded camelina production will exhibit yield shifts at the continental scale as temperature and precipitation deviate from current conditions. Yield data were compared with indices of climatic suitability to provide estimates of potential worldwide camelina productivity. This information was used to identify new areas where spring‐seeded camelina could be grown and areas that may permit expanded production, including eastern Europe, China, eastern Russia, Australia and New Zealand. Our model is the first to have taken a systematic approach to determine suitable regions for potential worldwide production of spring‐seeded camelina.

The genetic composition of founding populations is likely to play a key role in determining invasion success. Individual genotypes may differ in habitat preference and environmental tolerance, so their ability to colonize novel environments can be highly variable. Despite the importance of genetic variation on invasion success, its influence on the potential distribution of invaders is rarely investigated. Here, we integrate population genomics and ecological niche models (ENMs) into a single framework to predict the distribution of globally invasive common ragweed (Ambrosia artemisiifolia) in Australia. We identified three genetic clusters for ragweed and used these to construct cluster‐specific ENMs and characterize within‐species niche differentiation. The potential range of ragweed in Australia depended on the genetic composition and continent of origin of the introduced population. Invaders originating from warmer, wetter climates had a broader potential distribution than those from cooler, drier ones. By quantifying this change, we identified source populations most likely to expand the ragweed distribution. As prevention remains the most effective method of invasive species management, our work provides a valuable way of ranking the threat posed by different populations to better inform management decisions.

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.

Chestnut, Castanea sativa Miller (Fagales: Fagaceae), is an ecologically and economically important tree species of the forest ecosystem in Southern Europe, North-Western Europe, Western Asia, North Africa, and the Caucasus. The distributional range of chestnut in Europe has been highly modified by humans since ancient times. Biotic and abiotic factors have dramatically changed its distribution. Historic anthropogenic range expansion makes it difficult to identify habitat requirements for natural stands of chestnut. In the Caucasus, natural stands of chestnut survived in glacial forest refugia and landscapes that have been difficult for humans to colonize. To identify the species reliable habitat requirements, we estimated the relationship between climatic variables and 620 occurrence locations of natural chestnut stands from the Caucasus and validated the model using GBIF data from outside the Caucasus. We found that our best model is reasonably accurate and the data from the Caucasus characterize chestnut stands throughout the species range well.

The plants' geographic distribution is affected by natural or human‐induced climate change. Numerous studies at both the global and regional levels currently focus on the potential changes in plant distribution areas. Ecological niche modeling can help predict the likely distribution of species according to environmental variables under different climate scenarios. In this study, we predicted the potential geographic distributions of Quercus ilex L. (holm oak), a keystone species of the Mediterranean ecosystem, for the Last Interglacial period (LIG: ~130 Ka), the Last Glacial Maximum (LGM: ~22 Ka), mid‐Holocene (MH: ~6 Ka), and future climate scenarios (Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios) for 2050–2070 obtained from CCSM4 and MIROC‐ESM global climate scenarios respectively. The models were produced with algorithms from the R‐package “biomod2” and assessed by AUC of the receiver operating characteristic plot and true skill statistics. Aside from BIOCLIM (SRE), all model algorithms performed similarly and produced projections that are supported by good evaluation scores, although random forest (RF) slightly outperformed all the others. Additionally, distribution maps generated for the past period were validated through a comparison with pollen data acquired from the Neotoma Pollen Database. The results revealed that southern areas of the Mediterranean Basin, particularly coastal regions, served as long‐term refugia for Q. ilex, which was supported by fossil pollen data. Furthermore, the models suggest long‐term refugia role for Anatolia and we argue that Anatolia may have served as a founding population for the species. Future climate scenarios indicated that Q. ilex distribution varied by region, with some areas experiencing range contractions and others range expands. This study provides significant insights into the vulnerability of the Q. ilex to future climate change in the Mediterranean ecosystem and highlights the crucial role of Anatolia in the species' historical distribution.

Subtropical evergreen broad‐leaved forest (EBLF) is the predominant vegetation type in eastern China. However, the majority of the region it covers in eastern China was an arid area during the Paleogene. The temporal history and essential factors involved in the evolution of subtropical EBLFs in eastern China remain enigmatic. Here we report on the niche evolution of Quercus section Cyclobalanopsis, which appeared in south China and Japan during the Eocene and became a dominant component of subtropical EBLFs since the Miocene in eastern Asia, using integrative analysis of occurrences, climate data and a dated phylogeny of 35 species in Cyclobalanopsis. Species within clades Cyclobalanoides, Lamellosa, and Helferiana mainly exist in the Himalaya–Hengduan region, adapting to a plateau climate, while species within the other clades mainly live in eastern China under the control of the East Asian monsoon. Reconstructed history showed that significant divergence of climatic tolerance in Cyclobalanopsis began around 19 million years ago (Ma) in the early Miocene. Simultaneously, disparities in precipitation of wettest/warmest quarter and annual precipitation were markedly enhanced in Cyclobalanopsis, especially in the recent eastern clades. During the Miocene, the marked radiation of Cyclobalanopsis and many other dominant taxa of subtropical EBLFs strongly suggest the rapid formation and expansion of subtropical EBLFs in eastern China. Our research highlights that the intensification of the East Asian monsoon and subsequent occupation of new niches by the ancient clades already present in the south may have jointly promoted the formation of subtropical EBLFs in eastern China since the early Miocene.

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.