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Changes in Earth's climate are accelerating, prompting increasing calls to ensure that investments in ecological restoration and nature conservation accommodate such changes. To acknowledge this need, we propose the term \"ecological renovation\" to describe ecological management and nature conservation actions that actively allow for environmental change. To evaluate and progress the development of ecological renovation and related intervention options in a climate change context, we reviewed the literature and established a typology of options that have been proposed. We explored how these options address emerging principles underpinning climate-adapted conservation goals and whether the balance of approaches reflected in our typology is likely to be sufficient given expected rapid rates of climate change. Our typology recognizes a matrix of 23 intervention option types arranged on the basis of underpinning ecological mechanisms (\"ameliorate changing conditions\" or \"build adaptive capacity\") on one axis, and the nature of the tools used to manipulate them (\"low regrets\" or \"climate targeted\") on the other. Despite a burgeoning literature since 2008, we found that the majority of effort has consistently focused on low-regrets adaptation approaches that aim to build adaptive capacity. This is in many ways desirable, but a paradigm shift enabling greater attention to climate-targeted approaches is likely to be needed as climate change accelerates. When assessed against five emerging principles for setting nature conservation goals in a changing climate, only one option type could deliver to all five, and we identified a conflict between climate-targeted options and \"wildness\" values that calls for deeper evaluation. Importantly, much of the inference in the 473 reviewed studies was drawn from ecological reasoning and modeling, with only 16% offering new empirical evidence. We also noted significant biases toward North America and Europe, forest ecosystems, trees, and vertebrates. To address these limitations and help shift the paradigm toward humans as \"renovators\" rather than \"restorers\" of a prior world, we propose that ecological researchers contribute by (1) informing societal discourse toward adapting nature conservation goals to climate change, (2) adjusting and upscaling conservation planning to accommodate this suite of climate-adapted goals, and (3) reconceptualizing experimental approaches to increase empirical evidence and expedite innovation of tools to address change.

How does climate control river chemistry? Existing literature has examined extensively the response of river chemistry to short‐term weather conditions from event to seasonal scales. Patterns and drivers of long‐term, baseline river chemistry have remained poorly understood. Here we compile and analyze chemistry data from 506 minimally impacted rivers (412,801 data points) in the contiguous United States (CAMELS‐Chem) to identify patterns and drivers of river chemistry. Despite distinct sources and diverse reaction characteristics, a universal pattern emerges for 16 major solutes at the continental scale. Their long‐term mean concentrations (Cm) decrease with mean discharge (Qm), with elevated concentrations in arid climates and lower concentrations in humid climates, indicating overwhelming regulation by climate compared to local Critical Zone characteristics such as lithology and topography. To understand the CmQm pattern, a parsimonious watershed reactor model was solved by bringing together hydrology (storage–discharge relationship) and biogeochemical reaction theories from traditionally separate disciplines. The derivation of long‐term, steady state solutions lead to a power law form of CmQm relationships. The model illuminates two competing processes that determine mean solute concentrations: solute production by subsurface biogeochemical and chemical weathering reactions, and solute export (or removal) by mean discharge, the water flushing capacity dictated by climate and vegetation. In other words, watersheds function primarily as reactors that produce and accumulate solutes in arid climates, and as transporters that export solutes in humid climates. With space‐for‐time substitution, these results indicate that in places where river discharge dwindles in a warming climate, solute concentrations will elevate even without human perturbation, threatening water quality and aquatic ecosystems. Water quality deterioration therefore should be considered in the global calculation of future climate risks. Key Points Continental‐scale river chemistry data show that mean discharge predominantly regulates mean concentrations of 16 solutes A simple watershed hydro‐biogeochemical reactor model illuminates that river chemistry is driven by the relative rates of solute addition (by reactions and input) and solute export Where river discharge dwindles in a warmer climate, higher concentrations will deteriorate water quality even without human perturbations

Quantitative scenarios are coming of age as a tool for evaluating the impact of future socioeconomic development pathways on biodiversity and ecosystem services. We analyze global terrestrial, freshwater, and marine biodiversity scenarios using a range of measures including extinctions, changes in species abundance, habitat loss, and distribution shifts, as well as comparing model projections to observations. Scenarios consistently indicate that biodiversity will continue to decline over the 21st century. However, the range of projected changes is much broader than most studies suggest, partly because there are major opportunities to intervene through better policies, but also because of large uncertainties in projections.

Located in a monsoon domain, East Asia suffers devastating natural hazards induced by anomalous monsoon behaviors. East Asian monsoon (EAM) research has traditionally been a high priority for the Chinese climate community and is particularly challenging in a changing climate where the global mean temperature has been rising. Recent advances in studies of the variabilities and mechanisms of the EAM are reviewed in this paper, focusing on the interannual to interdecadal time scales. Some new results have been achieved in understanding the behaviors of the EAM, such as the evolution of the East Asian summer monsoon (EASM), including both its onset and withdrawal over the South China Sea, the changes in the northern boundary activity of the EASM, or the transitional climate zone in East Asia, and the cycle of the EASM and the East Asian winter monsoon and their linkages. In addition, understanding of the mechanism of the EAM variability has improved in several aspects, including the impacts of different types of ENSO on the EAM, the impacts from the Indian Ocean and Atlantic Ocean, and the roles of mid- to high-latitude processes. Finally, some scientific issues regarding our understanding of the EAM are proposed for future investigation.

BackgroundSoil organic carbon (SOC) plays a crucial role in the global carbon cycle and terrestrial ecosystem functions. It is widely known that climate change and soil water content (SWC) could influence the SOC dynamics; however, there are still debates about how climate change, especially climate warming, and SWC impact SOC. We investigated the spatiotemporal changes in SOC and its responses to climate warming and root-zone SWC change using the coupled hydro-biogeochemical model (SWAT-DayCent) and climate scenarios data derived under the three Representative Concentration Pathways (RCPs2.6, 4.5, and 8.5) from five downscaled Global Climate Models (GCMs) in a typical loess watershed––the Jinghe River Basin (JRB) on the Chinese Loess Plateau.ResultsThe air temperature would increase significantly during the future period (2017–2099), while the annual precipitation would increase by 2.0–13.1% relative to the baseline period (1976–2016), indicating a warmer and wetter future in the JRB. Driven by the precipitation variation, the root-zone SWC would also increase (by up to 27.9% relative to the baseline under RCP4.5); however, the SOC was projected to decrease significantly under the future warming climate. The combined effects of climate warming and SWC change could more reasonably explain the SOC loss, and this formed hump-shaped response surfaces between SOC loss and warming-SWC interactions under both RCP2.6 and 8.5, which can help explain diverse warming effects on SOC with changing SWC.ConclusionsThe study showed a significant potential carbon source under the future warmer and wetter climate in the JRB, and the SOC loss was largely controlled by future climate warming and the root-zone SWC as well. The hump-shaped responses of the SOC loss to climate warming and SWC change demonstrated that the SWC could mediate the warming effects on SOC loss, but this mediation largely depended on the SWC changing magnitude (drier or wetter soil conditions). This mediation mechanism about the effect of SWC on SOC would be valuable for enhancing soil carbon sequestration in a warming climate on the Loess Plateau.

The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision‐making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e., uncertainty) of the predictions and determine if the probability of an outcome is statistically significant. To enhance trustworthiness, we also spatially apply the two XAI techniques of Layer‐wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP) values. These XAI methods reveal the extent to which the BNN is suitable and/or trustworthy. Using two techniques gives a more holistic view of BNN skill and its uncertainty, as LRP considers neural network parameters, whereas SHAP considers changes to outputs. We verify these techniques using comparison with intuition from physical theory. The differences in explanation identify potential areas where new physical theory guided studies are needed. Plain Language Summary Understanding ocean dynamics and how they are affected by global heating is crucial for understanding climate change impacts. Neural networks are ideally suited to this problem, but do not explain how they make predictions nor express how certain they are of the predictions' accuracy, which considerably limits their trustworthiness for ocean science problems. Here, we address both issues by using a “Bayesian Neural Network” (BNN), which directly expresses prediction uncertainty, and applying explainable AI (XAI) techniques to explain how the BNN arrives at its prediction. The BNN provides a comprehensive overview more suited to addressing the core problem than that provided by classical neural networks. We also apply two XAI techniques (SHAP and LRP) to the BNN and evaluate their trustworthiness by comparing the similarities and differences between their explanations with intuition from physical theory. Any differences offer an opportunity to develop physical theory guided by what the BNN considers important. Key Points Novel use of a Bayesian Neural Network (BNN) to quantify uncertainty in ocean dynamical regime classifications, giving a holistic prediction Explaining the skill of a BNN using two techniques originating from two different classes of explainable AI: SHapley Additive exPlanation (SHAP) and Layer‐wise Relevance Propagation (LRP) Trustworthiness is evaluated by comparing similarities and differences between SHAP and LRP explanations with intuition from physical theory

Toxoplasma gondii is an obligate intracellular protozoan that poses a great threat to human health and economic well-being worldwide. The effects of environmental factors such as changing climate and human activities on the ecology of this protozoan are being discovered. Accumulated evidence shows that changes of these environmental factors can exert influence on the occurrence, transmission and distribution of T. gondii. This article reviews studies from different geographical regions with varying climates, social cultures and animal welfare standards. It aims to illustrate how these environmental factors work, highlighting their importance in influencing the ecology of T. gondii , as well as providing clues which may contribute to preventing transmission of this important zoonotic pathogen.

This research addresses the issue of the heritage preventive conservation in the perspective of energy sustainability, for contributing to the achievement of the Sustainable Development Goals (SDGs) and towards the EU Green Deal. The study analyses and compares four cases associated with different microclimate thresholds as suggested by the standard EN 16893:2018 (Cases 1–3) and as derived from the outputs of three degradation models for preserving paper, wood, and canvas paintings (Case 4). Weather-based indices (degree and gram days) were calculated to estimate trends in the potential energy demand of collection facilities in three European cities belonging to different Köppen-Geiger climate zones (Cfb, Csa, and Dfb), under recent past (1981–2010) and near/far future climate scenarios (2021–2050 and 2071–2100) from two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). The findings suggest that adapting facilities’ management strategies to focus on collections preservation can facilitate the achievement of 5 out of 17 SDGs, offering a viable alternative to costly energy retrofits and encouraging the development of shared solutions for similar facilities in the same climate zone. The results can contribute to inform the revision of EN 16893 and to face major challenges such as the preservation of paper collections in southern latitudes.

Climate change significantly impacts global agricultural productivity. Therefore, a more dynamic farming system is needed to enable farmers to better adapt to climate change while contributing to efforts to produce enough food to feed the growing world population. In the context of climate change, this study analyzed the empirical scientific literature on the link between climate-smart agriculture and farm productivity. To evaluate the relevant articles, the authors used the search term “climate-smart agriculture amidst climate change to enhance agricultural production (CSA-CCAP)” to find studies published between 2009 and March 2022 using innovative bibliometric techniques. One hundred and sixteen published papers in BibTeX format were downloaded for further analysis. The most successful selected CSA approaches in Africa, such as in the Congo Basin forest, including sustainable land management practices, water-efficient maize hybrids, and others, aim to counteract climate change with signs of 200 percent output gains. The findings showed an annual growth rate of about 19%, demonstrating that research on CSA-CCAP expanded over time during the study period. Nonetheless, the research output on CSA-CCAP varied, with 2021 accounting for 30%, followed by 2020 with 16% as of March 2022. The study concluded that boosting agricultural productivity in the face of climate change may be accomplished through CSA to end hunger, eradicate poverty, and improve people’s well-being.

Vegetation is sensitive to regional climate change especially in the arid area, and the effects of global warming on regional climate and ecological vegetation are complex and varied. Spatio-temporal variation of vegetation is closely related to the changing climate, detecting the correlation between the variation of climate and vegetation characteristics in time and spatial dimension is important for understanding the effects of climate change on vegetation ecosystem. Therefore, remote sensing and information entropy were proposed in the study to estimate the spatio-temporal variation of vegetation with normalized difference vegetation index (NDVI) and meteorological elements in the Nalenggele river basin, northwest China. The average NDVI value of the study area showed an increasing trend but more than 40% of the vegetation characteristic in this area showed a worse trend from 1987 to 2016. Temperature is the relatively prominent factor that affects the vegetation variation in this area, but only 12.25% of the vegetation showed strong correlation with temperature. The increasing temperature promoted the growth of vegetation in the oasis region and also caused the degradation of vegetation in the Gobi desert region to some extent. The variation of precipitation and humidity are not the key factors that control vegetation growth, less than 2% of the vegetation showed strong correlation with these two meteorological elements. The results of this study support the conclusion that the fragile vegetation ecosystem in the vast Gobi desert areas shows deteriorating trend in the past several decades closely to the changing climate.