Spatial gradients of species richness can be shaped by the interplay between historical and ecological factors. They might interact in particularly complex ways in heterogeneous mountainous landscapes with strong climatic and geological contrasts. We mapped the distribution of 171 lizard species to investigate species richness patterns for all species (171), diurnal species (101), and nocturnal species (70) separately. We related species richness with the historical (past climate change, mountain uplifting) and ecological variables (climate, topography and vegetation). We found that assemblages in the Western Zagros Mountains, north eastern and north western parts of Central Iranian Plateau have the highest number of lizard species. Among the investigated variables, annual mean temperature explained the largest variance for all species (10%) and nocturnal species (31%). For diurnal species, temperature change velocity shows strongest explained variance in observed richness pattern (26%). Together, our results reveal that areas with annual temperature of 15–20 °C, which receive 400–600 mm precipitation and experienced moderate level of climate change since the Last Glacial Maximum (LGM) have highest number of species. Documented patterns of our study provide a baseline for understanding the potential effect of ongoing climate change on lizard diversity in Iran.

Global changes pose both risks and opportunities to agriculture and forestry, and biological forecasts can inform future management strategies. Here, we investigate potential land-use opportunities arising from climate change for these sectors in Europe, and risks associated with the introduction and establishment of novel insect pests. Adopting a metaweb approach including all interaction links between 126 crops and forest tree species and 89 black-listed insect pest species, we show that the metawebs shift toward increased numbers of links and overlap of suitable area under climate change. Decomposing the metaweb across regions shows large saturation in southern Europe, while many novel interactions are expected for northern Europe. In light of the rising consumer awareness about human health and environmental impacts of food and wood production, the challenge will be to effectively exploit new opportunities to create diverse local agriculture and forestry while controlling pest species and reducing risks from pesticide use.

Seidl & Turner, 2022). Increasing summer aridity has been suggested as a main driver of changing forest fire regimes (Huang et al., 2020;Jolly et al., 2015;Williams et al., 2019). For instance, increasing aridity and the resultant dryer fuels led to a fivefold increase in area burned in California over the past 50 years (Williams et al., 2019).Extreme events like the Black Summer of 2019/2020 in Australia are expected to become more frequent (Abram et al., 2021) as increasing temperatures and changing precipitation patterns promote more extreme fire weather (Chiang et al., 2021;Jain et al., 2022;Vicente-Serrano et al., 2020). Increasing forest fire activity could have a number of negative effects on forest ecosystem functions, including a reduction in ecosystem carbon storage (Bowman et al., 2021;

Aim: To map the spatial variation of range sizes within sphingid moths, and to test hypotheses on its environmental control. In particular, we investigate effects of climate change velocity since the Pleistocene and the mid-Holocene, temperature and precipitation seasonality, topography, Pleistocene ice cover, and available land area.Location: Old World and Australasia, excluding smaller islands.Methods: We used fine-grained range maps (based on expert-edited distribution modelling) for all 972 sphingid moth species in the research region and calculated, at a grain size of 100 km, the median of range sizes of all species that co-occur in a pixel. Climate, topography and Pleistocene ice cover data were taken from publicly available sources. We calculated climate change velocities (CCV) for the last 21 kyr as well as 6 kyr. We compared the effects of seasonality and CCV on median range sizes with spatially explicit models while accounting for effects of elevation range, glaciation history and available land area.Results: Range sizes show a clear spatial pattern, with highest median values in deserts and arctic regions and lowest values in isolated tropical regions. Range sizes were only weakly related to absolute latitude (predicted by Rapoport's effect), but there was a strong north-south pattern of range size decline. Temperature seasonality emerged as the strongest environmental correlate of median range size, in univariate as well as multivariate models, whereas effects of CCV were weak and unstable for both time periods. These results were robust to variations in the parameters in alternative analyses, among them multivariate CCV.Main conclusions: Temperature seasonality is a strong correlate of spatial range size variation, while effects of longer-term temperature change, as captured by CCV, received much weaker support. K E Y W O R D S climate change velocity, Old World, range size, rapoport effect, seasonality, sphingid moths

Climate change and globalization affect the suitable conditions for agricultural crops and insect pests, threatening future food security. It remains unknown whether shifts in species' climatic suitability will be linear or rather non-linear, with crop exposure to pests suddenly increasing when a critical temperature threshold is crossed. Moreover, uncertainty of forecasts can arise because of the modelling approach based either on species distribution data or on physiological measurements. Here, we compared the predictions of two modelling approaches (physiological models and species distribution models) for forecasting the potential distribution of agricultural insect pests in Europe. Despite conceptual differences, we found good agreement overall between the two approaches. We further identified a potential regime change in pest pressure along a temperature gradient. With both modelling approaches, we found an inflection point in the number of pest species with suitable climatic conditions around a minimum temperature of the coldest month of −3°C. Our results could help decision-makers anticipate the onset of rising pest pressure and provide support for intensifying surveillance measures, particularly in regions where temperatures are already beyond the inflection point. K E Y W O R D S agricultural crop, climate change, insect pest, physiological model, species distribution model, temperature threshold | 6339 GRÜNIG et al.

A major challenge of agriculture is to improve the sustainability of food production systems in order to provide enough food for a growing human population. Pests and pathogens cause vast yield losses, while crop protection practices raise environmental and human health concerns. Decision support systems provide detailed information on optimal timing and necessity of crop protection interventions, but are often based on phenology models that are time‐, cost‐, and labor‐intensive in development. Here, we aim to develop a data‐driven approach for pest damage forecasting, relying on big data and deep learning algorithms. We present a framework for the development of deep neural networks for pest and pathogen damage classification and show their potential for predicting the phenology of damages. As a case study, we investigate the phenology of the pear leaf blister moth (Leucoptera malifoliella, Costa). We employ a set of 52,322 pictures taken during a period of 19 weeks and establish deep neural networks to categorize the images into six main damage classes. Classification tools achieved good performance scores overall, with differences between the classes indicating that the performance of deep neural networks depends on the similarity to other damages and the number of training images. The reconstructed damage phenology of the pear leaf blister moth matches mine counts in the field. We further develop statistical models to reconstruct the phenology of damages with meteorological data and find good agreement with degree‐day models. Hence, our study indicates a yet underexploited potential for data‐driven approaches to enhance the versatility and cost efficiency of plant pest and disease forecasting.

Snakebite is one of the largest risks from wildlife, however little is known about venomous snake distribution, spatial variation in snakebite risk, potential changes in snakebite risk pattern due to climate change, and vulnerable human population. As a consequence, management and prevention of snakebite is hampered by this lack of information. Here we used habitat suitability modeling for 10 medically important venomous snakes to identify high snakebite risk area under climate change in Iran. We identified areas with high snakebite risk in Iran and showed that snakebite risk will increase in some parts of the country. Our results also revealed that mountainous areas (Zagros, Alborz, Kopet–Dagh mountains) will experience highest changes in species composition. We underline that in order to improve snakebite management, areas which were identified with high snakebite risk in Iran need to be prioritized for the distribution of antivenom medication and awareness rising programs among vulnerable human population.

Snakebite is a global health problem and yearly snakebites have been estimated up to 5 million leading to about 100,000 deaths each year. While those numbers are showing that snakebite is one of the largest risks from wildlife, little is known about venomous snake distribution, spatial variation in snakebite risk, potential changes in snakebite risk pattern due to climate change, and vulnerable human population. As a consequence, management and prevention of snakebite is hampered by this lack of information. Previous studies suggest that habitat suitability models are effective tools in predicting snakebite risk areas under current and future climate and identifying vulnerable human population. Here we used an ensemble approach of five different habitat suitability modeling algorithms for 10 medically important venomous snakes to quantify snakebite risk pattern, map snakebite hotspots, calculate community composition changes and changes in vulnerability to snakebite in Iran under current and future climate (years 2041–2070 and 2071–2100). We identified areas with high snakebite risk in Iran and showed that snakebite risk will increase in some parts of the country. We also found mountainous areas (Zagros, Alborz, Kopet-Dagh mountains) will experience highest changes in species composition. We underline that in order to improve snakebite management, areas which were identified with high snakebite risk in Iran need to be prioritized for the distribution of antivenom medication and awareness rising programs among vulnerable human population.