Climate change can affect biotic interactions, and the impacts of climate on biotic interactions may vary across climate gradients. Climate affects biotic interactions through multiple drivers, although few studies have investigated multiple climate drivers in experiments. We examined the effects of experimental watering, warming, and predator access on leaf water content and herbivory rates of woolly bear caterpillars () on a native perennial plant, pacific silverweed (), at two sites across a gradient of precipitation in coastal California. Based on theory, we predicted that watering should increase herbivory at the drier end of the gradient, predation should decrease herbivory, and watering and warming should have positive interacting effects on herbivory. Consistent with our predictions, we found that watering only increased herbivory under drier conditions. However, watering increased leaf water content at both wetter and drier sites. Warming increased herbivory irrespective of local climate and did not interact with watering. Predation did not affect herbivory rates. Given predictions that the study locales will become warmer and drier with climate change, our results suggest that the effects of future warming and drying on herbivory may counteract each other in drier regions of the range of . Our findings suggest a useful role for range-limit theory and the stress-gradient hypothesis in predicting climate change effects on herbivory across stress gradients. Specifically, if climate change decreases stress, herbivory may increase, and vice versa for increasing stress. In addition, our work supports previous suggestions that multiple climate drivers are likely to have dampening effects on biotic interactions due to effects in different directions, though this is context-dependent.
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http://dx.doi.org/10.1002/ece3.7197 | DOI Listing |
J Environ Manage
December 2024
College of Forestry, Guizhou University, Guiyang, 550025, China; Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China. Electronic address:
In recent years, the rapid development of the global economy has led to an increasing impact of the ongoing climate warming phenomenon on the hydrological cycle. In this context, the runoff changes affected by human activities are more severe. This study classifies climate scenarios based on carbon emission levels into "low-carbon" (SSP1-2.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of Forest, Environment, and Climate Change, Chilika Development Authority, Barkul, Odisha, India.
Chlorophyll-a (Chla) is recognized as a key indicator of water quality and ecological health in aquatic ecosystems, offering valuable insights into ecosystem dynamics and changes over time. This study aimed to to develop and validate a robust ML model for estimating Chla using Landsat data, produce a time series of Chl a maps, and analyze the spatiotemporal variability of Chla in Chilika Lagoon, Asia's largest brackish water lagoon. Nine ML regression models, including Extreme Gradient Boost, Support Vector Regression, Random Forest, and Bagging Regression, were evaluated using Landsat imagery and field data.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of Botany, University of Jammu, Baba Saheb Ambedkar Road, Jammu Tawi, J&K, 180006, India.
The broad-scale inventories of alien species reveal macroecological patterns, but these often fall short in guiding local-level management strategies. Local authorities, tasked with on-the-ground management, require precise knowledge of the occurrence of invasive species tailored to their jurisdictional boundaries. What proves critical at the local scale may not hold the same significance at national or regional levels.
View Article and Find Full Text PDFJ Exp Bot
December 2024
Molecular Systems Biology Lab (MOSYS), Department of Functional and Evolutionary Ecology, University of Vienna, Djerassiplatz 1, 1030 Vienna, Austria.
Our current agricultural system faces a perfect storm-climate change, burgeoning population, and unpredictable outbreaks like COVID-19 disrupt food production, particularly for vulnerable populations in developing countries. A paradigm shift in agriculture practices is needed to tackle these issues. One solution is the diversification of crop production.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, Tamil Nadu, India.
Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.
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