Nonlinear shifts in infectious rust disease due to climate change

Nonlinear effects of temperature on mosquito parasite infection across a large geographic climate gradient

Temperature drives ectothermic host - parasite interactions, making them particularly sensitive to climatic variation and change. To isolate the role of temperature, lab-based studies are increasingly used to assess and forecast disease risk under current and future climate conditions. However, in the field, the effects of temperature on parasitism may be mediated by other sources of variation, including local adaptation. To address the key knowledge gaps of how temperature influences host - parasite interactions and whether thermal responses measured in controlled experiments capture infection across temperature gradients in nature, we paired an extensive field survey of parasitism-by the ciliate Lambornella clarki on its tree hole mosquito host, Aedes sierrensis -with laboratory experiments describing parasitism thermal performance curves (TPCs) for six host populations from varying climates. We also investigated the mechanisms underlying the thermal biology of the host - parasite interaction by separately measuring TPCs for infection, host immunity, and parasite growth rates. Along the west coast of North America, across an 1100 km climate gradient spanning 12°C mean rainy season temperature variation, we found that parasitism peaked at intermediately cold temperatures, and was consistent both between field seasons and with the lab experiment results. The experiments produced no evidence of host intraspecific variation in temperature sensitivity to parasitism. Importantly, parasitism peaked at temperatures below the thermal optimum for free-living L. clarki due to the balance of temperature effects on parasite growth and reproduction against the strength of the host melanization immune response. The results suggest that nonlinear responses to temperature drive parasitism in nature, and that simple lab and field studies can accurately capture the thermal biology of multilayered host - parasite interactions. Data and code for this submission are provided on Dryad: http://datadryad.org/stash/share/CfZkk4LsJzljetJJnFZMDMrjuciTXMxrkrc95I2J3tA .

PHI-base - the multi-species pathogen-host interaction database in 2025

The Pathogen-Host Interactions Database (PHI-base) has, since 2005, provided manually curated genes from fungal, bacterial and protist pathogens that have been experimentally verified to have important pathogenicity, virulence and/or effector functions during different types of interactions involving human, animal, plant, invertebrate and fungal hosts. PHI-base provides phenotypic annotation and genotypic information for both native and model host interactions, including gene alterations that do not alter the phenotype of the interaction. In this article, we describe major updates to PHI-base. The latest version of PHI-base, 4.17, contains a 19% increase in genes and a 23% increase in interactions relative to version 4.12 (released September 2022). We also describe the unification of data in PHI-base 4 with the data curated from a new curation workflow (PHI-Canto), which forms the first complete release of PHI-base version 5.0. Additionally, we describe adding support for the Frictionless Data framework to PHI-base 4 datasets, new ways of sharing interaction data with the Ensembl database, an analysis of the conserved orthologous genes in PHI-base, and the increasing variety of research studies that make use of PHI-base. PHI-base version 4.17 is freely available at www.phi-base.org and PHI-base version 5.0 is freely available at phi5.phi-base.org.

Causal Inference With Observational Data and Unobserved Confounding Variables

Experiments have long been the gold standard for causal inference in Ecology. As Ecology tackles progressively larger problems, however, we are moving beyond the scales at which randomised controlled experiments are feasible. To answer causal questions at scale, we need to also use observational data -something Ecologists tend to view with great scepticism. The major challenge using observational data for causal inference is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders-known or unknown-lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this omitted variable bias, other disciplines have developed rigorous approaches for causal inference from observational data that flexibly control for broad suites of confounding variables. We show how ecologists can harness some of these methods-causal diagrams to identify confounders coupled with nested sampling and statistical designs-to reduce risks of omitted variable bias. Using an example of estimating warming effects on snails, we show how current methods in Ecology (e.g., mixed models) produce incorrect inferences due to omitted variable bias and how alternative methods can eliminate it, improving causal inferences with weaker assumptions. Our goal is to expand tools for causal inference using observational and imperfect experimental data in Ecology.

Impacts of Weather Anomalies and Climate on Plant Disease

Predicting the effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate and weather anomalies by compiling a global database (4339 plant-disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesised that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported. In wild systems, disease prevalence peaked when the temperature was 2.7°C warmer than the historical average for the same time of year. We also found evidence of a negative interactive effect between weather anomalies and climate in wild systems, consistent with the idea that climate maladaptation can be an important driver of disease outbreaks. Temperature and precipitation had relatively little explanatory power in agricultural systems, though we observed a significant positive effect of current temperature. These results indicate that disease pressure in wild plants is sensitive to nonlinear effects of weather, weather anomalies and their interaction with historical climate. In contrast, warmer temperatures drove risks for agricultural plant disease outbreaks within the temperature range examined regardless of historical climate, suggesting vulnerability to ongoing climate change.

Foundations and Future Directions for Causal Inference in Ecological Research

Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges. Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers. We introduce approaches for causal inference, discussing the main frameworks for counterfactual causal inference, how causal inference differs from other research aims and key challenges; the application of causal inference in experimental and quasi-experimental study designs; appropriate interpretation of the results of causal inference approaches given their assumptions and biases; foundational papers; and the data requirements and trade-offs between internal and external validity posed by different designs. We highlight that these designs generally prioritise internal validity over generalisability. Finally, we identify opportunities and considerations for ecologists to further integrate causal inference with synthesis science and meta-analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference.

Carbon, climate, and natural disturbance: a review of mechanisms, challenges, and tools for understanding forest carbon stability in an uncertain future

In this review, we discuss current research on forest carbon risk from natural disturbance under climate change for the United States, with emphasis on advancements in analytical mapping and modeling tools that have potential to drive research for managing future long-term stability of forest carbon. As a natural mechanism for carbon storage, forests are a critical component of meeting climate mitigation strategies designed to combat anthropogenic emissions. Forests consist of long-lived organisms (trees) that can store carbon for centuries or more. However, trees have finite lifespans, and disturbances such as wildfire, insect and disease outbreaks, and drought can hasten tree mortality or reduce tree growth, thereby slowing carbon sequestration, driving carbon emissions, and reducing forest carbon storage in stable pools, particularly the live and standing dead portions that are counted in many carbon offset programs. Many forests have natural disturbance regimes, but climate change and human activities disrupt the frequency and severity of disturbances in ways that are likely to have consequences for the long-term stability of forest carbon. To minimize negative effects and maximize resilience of forest carbon, disturbance risks must be accounted for in carbon offset protocols, carbon management practices, and carbon mapping and modeling techniques. This requires detailed mapping and modeling of the quantities and distribution of forest carbon across the United States and hopefully one day globally; the frequency, severity, and timing of disturbances; the mechanisms by which disturbances affect carbon storage; and how climate change may alter each of these elements. Several tools (e.g. fire spread models, imputed forest inventory models, and forest growth simulators) exist to address one or more of the aforementioned items and can help inform management strategies that reduce forest carbon risk, maintain long-term stability of forest carbon, and further explore challenges, uncertainties, and opportunities for evaluating the continued potential of, and threats to, forests as viable mechanisms for forest carbon storage, including carbon offsets. A growing collective body of research and technological improvements have advanced the science, but we highlight and discuss key limitations, uncertainties, and gaps that remain.

A Mosquito Parasite Is Locally Adapted to Its Host but Not Temperature

AbstractClimate change will alter interactions between parasites and their hosts. Warming may affect patterns of local adaptation, shifting the environment to favor the parasite or host and thus changing the prevalence of disease. We assessed local adaptation to hosts and temperature in the facultative ciliate parasite Lambornella clarki, which infects the western tree hole mosquito Aedes sierrensis. We conducted laboratory infection experiments with mosquito larvae and parasites collected from across a climate gradient, pairing sympatric or allopatric populations across three temperatures that were either matched or mismatched to the source environment. Lambornella clarki parasites were locally adapted to their hosts, with 2.6 times higher infection rates on sympatric populations compared with allopatric populations, but they were not locally adapted to temperature. Infection peaked at the intermediate temperature of 12.5°C, notably lower than the optimum temperature for free-living L. clarki growth, suggesting that the host's immune response can play a significant role in mediating the outcome of infection. Our results highlight the importance of host selective pressure on parasites, despite the impact of temperature on infection success.

High temperatures reduce growth, infection, and transmission of a naturally occurring fungal plant pathogen

Climate change is rapidly altering the distribution of suitable habitats for many species as well as their pathogenic microbes. For many pathogens, including vector-borne diseases of humans and agricultural pathogens, climate change is expected to increase transmission and lead to pathogen range expansions. However, if pathogens have a lower heat tolerance than their host, increased warming could generate so-called thermal refugia for hosts. Predicting the outcomes of warming on disease transmission requires detailed knowledge of the thermal tolerances of both the host and the pathogen. Such thermal tolerance studies are generally lacking for fungal pathogens of wild plant populations, despite the fact that plants form the base of all terrestrial communities. Here, we quantified three aspects of the thermal tolerance (growth, infection, and propagule production) of the naturally occurring fungal pathogen Microbotryum lychnidis-dioicae, which causes a sterilizing anther-smut disease on the herbaceous plant Silene latifolia. We also quantified two aspects of host thermal tolerance: seedling survival and flowering rate. We found that temperatures >30°C reduced the ability of anther-smut spores to germinate, grow, and conjugate in vitro. In addition, we found that high temperatures (30°C) during or shortly after the time of inoculation strongly reduced the likelihood of infection in seedlings. Finally, we found that high summer temperatures in the field temporarily cured infected plants, likely reducing transmission. Notably, high temperatures did not reduce survival or flowering of the host plants. Taken together, our results show that the fungus is considerably more sensitive to high temperatures than its host plant. A warming climate could therefore result in reduced disease spread or even local pathogen extirpation, leading to thermal refugia for the host.

Disaster Plant Pathology: Smart Solutions for Threats to Global Plant Health from Natural and Human-Driven Disasters

Disaster plant pathology addresses how natural and human-driven disasters impact plant diseases and the requirements for smart management solutions. Local to global drivers of plant disease change in response to disasters, often creating environments more conducive to plant disease. Most disasters have indirect effects on plant health through factors such as disrupted supply chains and damaged infrastructure. There is also the potential for direct effects from disasters, such as pathogen or vector dispersal due to floods, hurricanes, and human migration driven by war. Pulse stressors such as hurricanes and war require rapid responses, whereas press stressors such as climate change leave more time for management adaptation but may ultimately cause broader challenges. Smart solutions for the effects of disasters can be deployed through digital agriculture and decision support systems supporting disaster preparedness and optimized humanitarian aid across scales. Here, we use the disaster plant pathology framework to synthesize the effects of disasters in plant pathology and outline solutions to maintain food security and plant health in catastrophic scenarios. We recommend actions for improving food security before and following disasters, including (i) strengthening regional and global cooperation, (ii) capacity building for rapid implementation of new technologies, (iii) effective clean seed systems that can act quickly to replace seed lost in disasters, (iv) resilient biosecurity infrastructure and risk assessment ready for rapid implementation, and (v) decision support systems that can adapt rapidly to unexpected scenarios. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The mediating role of pro-environmental attitude and intention on the translation from climate change health risk perception to pro-environmental behavior

Climate change is a serious environmental issue appearing in China. As a public service institution operating around the clock, the negative impact of hospitals on the environment is evident, promoting their workers' pro-environmental behavior (PEB) through increasing climate change health risk perception (CHRP) is an effective method to protect the environment and achieve sustainable development. This study investigates how CHRP shapes pro-environmental attitude (PEA), pro-environmental intention (PEI), and pro-environmental behavior (PEB) among hospital workers. Using structural equation modeling (SEM) to determine the chain of causation from CHRP to PEB among hospital workers. The result shows that CHRP positively affects PEA and PEI, and PEI positively affects their PEB. In addition, although CHRP has no significant direct effect on PEB, it can play a crucial indirect effect through the mediating role of PEI. Moreover, the result of multiple regression shows that there are significant differences regarding PEA, PEI, and PEB.

Global warming significantly increases the risk of Pierce's disease epidemics in European vineyards

Pierce's disease (PD) is a vector-borne disease caused by the bacteria Xylella fastidiosa, which affects grapevines in the Americas. Currently, vineyards in continental Europe, the world's largest producer of quality wine, have not yet been affected by PD. However, climate change may alter this situation. Here we incorporate the latest regional climate change projections into a climate-driven epidemiological model to assess the risk of PD epidemics in Europe for different levels of global warming. We found a significant increase in risk above + 2 ∘ C in the main wine-producing regions of France, Italy and Portugal, in addition to a critical tipping point above + 3 ∘ C for the possible spread of PD beyond the Mediterranean. The model identifies decreasing risk trends in Spain, as well as contrasting patterns across the continent with different velocities of risk change and epidemic growth rates. Although there is some uncertainty in model projections over time, spatial patterns of risk are consistent across different climate models. Our study provides a comprehensive analysis of the future of PD at multiple spatial scales (country, Protected Designation of Origin and vineyard), revealing where, why and when PD could become a new threat to the European wine industry.

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

The energy-water limitation threshold explains divergent drought responses in tree growth, needle length, and stable isotope ratios

Predicted increases in extreme droughts will likely cause major shifts in carbon sequestration and forest composition. Although growth declines during drought are widely documented, an increasing number of studies have reported both positive and negative responses to the same drought. These divergent growth patterns may reflect thresholds (i.e., nonlinear responses) promoted by changes in the dominant climatic constraints on tree growth. Here we tested whether stemwood growth exhibited linear or nonlinear responses to temperature and precipitation and whether stemwood growth thresholds co-occurred with multiple thresholds in source and sink processes that limit tree growth. We extracted 772 tree cores, 1398 needle length records, and 1075 stable isotope samples from 27 sites across whitebark pine's (Pinus albicaulis Engelm.) climatic niche in the Sierra Nevada. Our results indicated that a temperature threshold in stemwood growth occurred at 8.4°C (7.12-9.51°C; estimated using fall-spring maximum temperature). This threshold was significantly correlated with thresholds in foliar growth, as well as carbon (δ13 C) and nitrogen (δ15 N) stable isotope ratios, that emerged during drought. These co-occurring thresholds reflected the transition between energy- and water-limited tree growth (i.e., the E-W limitation threshold). This transition likely mediated carbon and nutrient cycling, as well as important differences in growth-defense trade-offs and drought adaptations. Furthermore, whitebark pine growing in energy-limited regions may continue to experience elevated growth in response to climate change. The positive effect of warming, however, may be offset by growth declines in water-limited regions, threatening the long-term sustainability of the recently listed whitebark pine species in the Sierra Nevada.

Growth, drought response, and climate-associated genomic structure in whitebark pine in the Sierra Nevada of California

Whitebark pine (Pinus albicaulis Engelm.) has experienced rapid population declines and is listed as threatened under the Endangered Species Act in the United States. Whitebark pine in the Sierra Nevada of California represents the southernmost end of the species' distribution and, like other portions of its range, faces threats from an introduced pathogen, native bark beetles, and a rapidly warming climate. Beyond these chronic stressors, there is also concern about how this species will respond to acute stressors, such as drought. We present patterns of stem growth from 766 large (average diameter at breast height >25 cm), disease-free whitebark pine across the Sierra Nevada before and during a recent period of drought. We contextualize growth patterns using population genomic diversity and structure from a subset of 327 trees. Sampled whitebark pine generally had positive to neutral stem growth trends from 1970 to 2011, which was positively correlated with minimum temperature and precipitation. Indices of stem growth during drought years (2012 to 2015) relative to a predrought interval were mostly positive to neutral at our sampled sites. Individual tree growth response phenotypes appeared to be linked to genotypic variation in climate-associated loci, suggesting that some genotypes can take better advantage of local climatic conditions than others. We speculate that reduced snowpack during the 2012 to 2015 drought years may have lengthened the growing season while retaining sufficient moisture to maintain growth at most study sites. Growth responses may differ under future warming, however, particularly if drought severity increases and modifies interactions with pests and pathogens.

A mosquito parasite is locally adapted to its host but not temperature

Climate change will alter interactions between parasites and their hosts. Warming may affect patterns of local adaptation, shifting the environment to favor the parasite or host and thus changing the prevalence of disease. We assessed local adaptation in the facultative ciliate parasite Lambornella clarki, which infects the western tree hole mosquito Aedes sierrensis. We conducted laboratory infection experiments with mosquito larvae and parasites collected from across a climate gradient, pairing sympatric or allopatric populations across three temperatures that were either matched or mismatched to the source environment. L. clarki parasites were locally adapted to their hosts, with 2.6x higher infection rates on sympatric compared to allopatric populations, but were not locally adapted to temperature. Infection peaked at the intermediate temperature of 13°C. Our results highlight the importance of host selective pressure on parasites, despite the impact of temperature on infection success.

Xanthomonas infection and ozone stress distinctly influence the microbial community structure and interactions in the pepper phyllosphere

While the physiological and transcriptional response of the host to biotic and abiotic stresses have been intensely studied, little is known about the resilience of associated microbiomes and their contribution towards tolerance or response to these stresses. We evaluated the impact of elevated tropospheric ozone (O3), individually and in combination with Xanthomonas perforans infection, under open-top chamber field conditions on overall disease outcome on resistant and susceptible pepper cultivars, and their associated microbiome structure, function, and interaction network across the growing season. Pathogen infection resulted in a distinct microbial community structure and functions on the susceptible cultivar, while concurrent O3 stress did not further alter the community structure, and function. However, O3 stress exacerbated the disease severity on resistant cultivar. This altered diseased severity was accompanied by enhanced heterogeneity in associated Xanthomonas population counts, although no significant shift in overall microbiota density, microbial community structure, and function was evident. Microbial co-occurrence networks under simultaneous O3 stress and pathogen challenge indicated a shift in the most influential taxa and a less connected network, which may reflect the altered stability of interactions among community members. Increased disease severity on resistant cultivar may be explained by such altered microbial co-occurrence network, indicating the altered microbiome-associated prophylactic shield against pathogens under elevated O3. Our findings demonstrate that microbial communities respond distinctly to individual and simultaneous stressors, in this case, O3 stress and pathogen infection, and can play a significant role in predicting how plant-pathogen interactions would change in the face of climate change.

Puccinia striiformis f. sp. tritici effectors in wheat immune responses

The obligate biotrophic fungus Puccinia striiformis f. sp. tritici, which causes yellow (stripe) rust disease, is among the leading biological agents resulting in tremendous yield losses on global wheat productions per annum. The combatting strategies include, but are not limited to, fungicide applications and the development of resistant cultivars. However, evolutionary pressure drives rapid changes, especially in its "effectorome" repertoire, thus allowing pathogens to evade and breach resistance. The extracellular and intracellular effectors, predominantly secreted proteins, are tactical arsenals aiming for many defense processes of plants. Hence, the identity of the effectors and the molecular mechanisms of the interactions between the effectors and the plant immune system have long been targeted in research. The obligate biotrophic nature of P. striiformis f. sp. tritici and the challenging nature of its host, the wheat, impede research on this topic. Next-generation sequencing and novel prediction algorithms in bioinformatics, which are accompanied by in vitro and in vivo validation approaches, offer a speedy pace for the discovery of new effectors and investigations of their biological functions. Here, we briefly review recent findings exploring the roles of P. striiformis f. sp. tritici effectors together with their cellular/subcellular localizations, host responses, and interactors. The current status and the challenges will be discussed. We hope that the overall work will provide a broader view of where we stand and a reference point to compare and evaluate new findings.

Invasive species do not exploit early growing seasons in burned tallgrass prairies

Invasive species management is key to conserving critically threatened native prairie ecosystems. While prescribed burning is widely demonstrated to increase native diversity and suppress invasive species, elucidating the conditions under which burning is most effective remains an ongoing focus of applied prairie ecology research. Understanding how conservation management interacts with climate is increasingly pressing, because climate change is altering weather conditions and seasonal timing around the world. Increasingly early growing seasons due to climate change are shifting the timing and availability of resources and niche space, which may disproportionately advantage invasive species and influence the outcome of burning. We estimated the effects of burning, start time of the growing season, and their interaction on invasive species relative cover and frequency, two metrics for species abundance and dominance. We used 25 observed prairie sites and 853 observations of 267 transects spread throughout Minnesota, USA from 2010 to 2019 to conduct our analysis. Here, we show that burning reduced the abundance of invasive cool-season grasses, leading to reduced abundance of invasive species as a whole. This reduction persisted over time for invasive cover but quickly waned for their frequency of occurrence. Additionally, and contrary to expectations that early growing season starts benefit invasive species, we found evidence that later growing season starts increased the abundance of some invasive species. However, the effects of burning on plant communities were largely unaltered by the timing of the growing season, although earlier growing season starts weakened the effectiveness of burning on Kentucky bluegrass (Poa pratensis) and smooth brome (Bromus inermis), two of the most dominant invasive species in the region. Our results suggest that prescribed burning will likely continue to be a useful conservation tool in the context of earlier growing season starts, and that changes to growing season timing will not be a primary mechanism driving increased invasion due to climate change in these ecosystems. We propose that future research seek to better understand abiotic controls on invasive species phenology in managed systems and how burning intensity and timing interact with spring conditions.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Interaction of drought- and pathogen-induced mortality in Norway spruce and Scots pine

Pathogenic diseases frequently occur in drought-stressed trees. However, their contribution to the process of drought-induced mortality is poorly understood. We combined drought and stem inoculation treatments to study the physiological processes leading to drought-induced mortality in Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) saplings infected with Heterobasidion annosum s.s. We analysed the saplings' water status, gas exchange, nonstructural carbohydrates (NSCs) and defence responses, and how they related to mortality. Saplings were followed for two growing seasons, including an artificially induced 3-month dormancy period. The combined drought and pathogen treatment significantly increased spruce mortality; however, no interaction between these stressors was observed in pine, although individually each stressor caused mortality. Our results suggest that pathogen infection decreased carbon reserves in spruce, reducing the capacity of saplings to cope with drought, resulting in increased mortality rates. Defoliation, relative water content and the starch concentration of needles were predictors of mortality in both species under drought and pathogen infection. Infection and drought stress create conflicting needs for carbon to compartmentalize the pathogen and to avoid turgor loss, respectively. Heterobasidion annosum reduces the functional sapwood area and shifts NSC allocation patterns, reducing the capacity of trees to cope with drought.

Elevating Air Temperature May Enhance Future Epidemic Risk of the Plant Pathogen Phytophthora infestans

Knowledge of pathogen adaptation to global warming is important for predicting future disease epidemics and food production in agricultural ecosystems; however, the patterns and mechanisms of such adaptation in many plant pathogens are poorly understood. Here, population genetics combined with physiological assays and common garden experiments were used to analyze the genetics, physiology, and thermal preference of pathogen aggressiveness in an evolutionary context using 140 Phytophthora infestans genotypes under five temperature regimes. Pathogens originating from warmer regions were more thermophilic and had a broader thermal niche than those from cooler regions. Phenotypic plasticity contributed ~10-fold more than heritability measured by genetic variance. Further, experimental temperatures altered the expression of genetic variation and the association of pathogen aggressiveness with the local temperature. Increasing experimental temperature enhanced the variation in aggressiveness. At low experimental temperatures, pathogens from warmer places produced less disease than those from cooler places; however, this pattern was reversed at higher experimental temperatures. These results suggest that geographic variation in the thermal preferences of pathogens should be included in modeling future disease epidemics in agricultural ecosystems in response to global warming, and greater attention should be paid to preventing the movement of pathogens from warmer to cooler places.

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future.

A plant disease image using convolutional recurrent neural network procedure intended for big data plant classification

The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%).