Most ecologists have used climate change, as an omnipresent pressure, to support their findings in researching the vulnerability of specific taxa, communities, or ecosystems. However, there is a widespread lack of long-term biological, biocoenological, or community data of periods longer than several years to ascertain patterns as to how climate change affects communities. Since the 1950s, southern Europe has faced an ongoing trend of drying and loss of precipitation. A 13-year research program in the Dinaric karst ecoregion of Croatia aimed to comprehensively track emergence patterns of freshwater insects (true flies: Diptera) in a pristine aquatic environment. Three sites, spring, upper, and lower tufa barriers (calcium carbonate barriers on a barrage lake system that act as natural damns), were sampled monthly over 154 months. This coincided with a severe drought event in 2011/2012. This was the most significant drought (very low precipitation rates for an extended period of time) in the Croatian Dinaric ecoregion since the start of detailed records in the early 20th century. Significant shifts in dipteran taxa occurrence were determined using indicator species analysis. Patterns of seasonal and yearly dynamics were presented as Euclidian distance metrics of similarity in true fly community composition compared at increasing time intervals, to ascertain the degree of temporal variability of similarity within the community of a specific site and to define patterns of similarity change over time. Analyses detected significant shifts in community structure linked to changes in discharge regimes, especially to the drought period.
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http://dx.doi.org/10.3390/biology12040590 | 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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