Giuseppe De Cristoforis

Without appropriate and ongoing management interventions, weeds will continue to economically and environmentally disadvantage agricultural and natural ecosystems. For these management strategies to have long‐term sustained success, they need to carefully consider the biological aspects of the targeted weed. These strategies will also need to consider potential adaptations evolved by the targeted weed in response to a range of selection pressures imposed by anthropogenetic factors, climate change, changing environmental conditions, and inappropriate or unsuccessful management regimes. One group of weeds that has been observed to readily adapt to a wide range of conditions and has shown considerable challenges in their management is invasive grasses. Adding to these challenges is that several invasive grasses have also developed resistance to a range of herbicide modes of action, which, to date, has been one of the most commonly used methods of control. To address these challenges, this review explores the biology and ecology of the globally invasive annuals Echinochloa crus‐galli (Barnyard grass) and Panicum capillare (Witchgrass), and the perennial Sorghum halepense (Johnson grass) to identify (i) the most suitable management options for their control and (ii) potential research gaps that may assist in the future management direction of these species. Based on the findings of this review, it is clear that an integrated management approach that targets different aspects of the plant's biology, in combination with early detection and treatment and ongoing surveillance, is necessary for the long‐term control of these species. Although a combination of methods appears promising, further investigation still is required to evaluate their efficiency and long‐term success in a changing environment, all of which are further discussed within this review.

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.

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.

Morphometry and diagnosis of Rhinomias forticornis (Boheman, 1842) are given. Distribution of Rhinomias forticornis is analyzed using known occurrences, original ecological data, correlative species distribution modeling with aspect on Last Glacial Maximum environment are given. To achieve a more re…

The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, w…

Every species experiences limits to its geographic distribution. Some evolutionary models predict that populations at range edges are less well‐adapted to their local environments due to drift, expansion load, or swamping gene flow from the range interior. Alternatively, populations near range edges…

Marked by the expansion of ice sheets in the high latitudes, the intensification of Northern Hemisphere glaciation across the Plio/Pleistocene transition at ~ 2.7 Ma represents a critical interval of late Neogene climate evolution. To date, the characteristics of climate change in North America duri…

Aim: When alien species are introduced to new ranges, climate or trait mismatches may initially constrain their population growth. However, inter‐ and intraspecific selection in the new environment should cause population growth rates to increase with residence time. Using a species‐for‐time approac…

Aim: Geographic distributions of mushroom species remain poorly understood despite their importance for advancing our understanding of the habitat requirements, species interactions and ecosystem functions of this key group of organisms. Here, we estimate geographic range extents (maximum within‐spe…

The uptake and deposition of silicon (Si) as silica phytoliths is common among land plants and is associated with a variety of functions. Among these, herbivore defense has received significant attention, particularly with regards to grasses and grasslands. Grasses are well known for their high sili…