Multivariate climate departures have outpaced univariate changes across global lands

Biodiversity conservation in the context of climate change: Facing challenges and management strategies

Biodiversity conservation amidst the uncertainty of climate change presents unique challenges that necessitate precise management strategies. The study reported here was aimed at refining understanding of these challenges and to propose specific, actionable management strategies. Employing a quantitative literature analysis, we meticulously examined 1268 research articles from the Web of Science database between 2005 and 2023. Through Cite Spaces and VOS viewer software, we conducted a bibliometric analysis and thematic synthesis to pinpoint emerging trends, key themes, and the geographical distribution of research efforts. Our methodology involved identifying patterns within the data, such as frequency of keywords, co-authorship networks, and citation analysis, to discern the primary focus areas within the field. This approach allowed us to distinguish between research concentration areas, specifically highlighting a predominant interest in Environmental Sciences Ecology (67.59 %) and Biodiversity Conservation (22.63 %). The identification of adaptive management practices and ecosystem services maintenance are central themes in the research from 2005 to 2023. Moreover, challenges such as understanding phenological shifts, invasive species dynamics, and anthropogenic pressures critically impact biodiversity conservation efforts. Our findings underscore the urgent need for precise, data-driven decision-making processes in the face of these challenges. Addressing the gaps identified, our study proposes targeted solutions, including the establishment of germplasm banks for at-risk species, the development of advanced genomic and microclimate models, and scenario analysis to predict and mitigate future conservation challenges. These strategies are aimed at enhancing the resilience of biodiversity against the backdrop of climate change through integrated, evidence-based approaches. By leveraging the compiled and analyzed data, this study offers a foundational framework for future research and practical action in biodiversity conservation strategies, demonstrating a path forward through detailed analysis and specified solutions.

Perspectives on the timing of ecosystem collapse in a changing climate

Climate change is one of the most important drivers of ecosystem change, the global-scale impacts of which will intensify over the next 2 decades. Estimating the timing of unprecedented changes is not only challenging but is of great importance for the development of ecosystem conservation guidelines. Time of emergence (ToE) (point at which climate change can be differentiated from a previous climate), a widely applied concept in climatology studies, provides a robust but unexplored approach for assessing the risk of ecosystem collapse, as described by the C criterion of the International Union for Conservation of Nature's Red List of Ecosystems (RLE). We identified 3 main theoretical considerations of ToE for RLE assessment (degree of stability, multifactorial instead of one-dimensional analyses, and hallmarks of ecosystem collapse) and 4 sources of uncertainty when applying ToE methodology (intermodel spread, historical reference period, consensus among variables, and consideration of different scenarios), which aims to avoid misuse and errors while promoting a proper application of the framework by scientists and practitioners. The incorporation of ToE for the RLE assessments adds important information for conservation priority setting that allows prediction of changes within and beyond the time frames proposed by the RLE.

California's 2023 snow deluge: Contextualizing an extreme snow year against future climate change

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term "snow deluge" and use anomalously high snowpack in California's Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California's 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges-which we define as the 20 y April 1 SWE event-are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

Sparse modeling for climate variable selection across trophic levels

Understanding how populations respond to climate is fundamentally important to many questions in ecology, evolution, and conservation biology. Climate is complex and multifaceted, with aspects affecting populations in different and sometimes unexpected ways. Thus, when measuring the changing climate it is important to consider the complexity of the phenomenon and the number of ways it can be characterized through different metrics. We used a Bayesian sparse modeling approach to select among 80 metrics of climate and applied the approach to 19 datasets of bird, insect, and plant population responses to abiotic conditions as case studies of how the method can be applied for climate variable selection in a time series context. For phenological datasets, mean spring temperature was frequently selected as an important climate driver, while selected predictors were more diverse for population metrics such as abundance or reproductive success. The climate variable selection approach presented here can help to identify potential climate metrics when there is limited physiological or mechanistic information to make an a priori variable selection, and is broadly applicable across studies on population responses to climate.

A species' response to spatial climatic variation does not predict its response to climate change

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here, we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

Burmese pythons in Florida: A synthesis of biology, impacts, and management tools

Burmese pythons (Python molurus bivittatus) are native to southeastern Asia, however, there is an established invasive population inhabiting much of southern Florida throughout the Greater Everglades Ecosystem. Pythons have severely impacted native species and ecosystems in Florida and represent one of the most intractable invasive-species management issues across the globe. The difficulty stems from a unique combination of inaccessible habitat and the cryptic and resilient nature of pythons that thrive in the subtropical environment of southern Florida, rendering them extremely challenging to detect. Here we provide a comprehensive review and synthesis of the science relevant to managing invasive Burmese pythons. We describe existing control tools and review challenges to productive research, identifying key knowledge gaps that would improve future research and decision making for python control.

Field observations and remote assessment identify climate change, recreation, invasive species, and livestock as top threats to critically imperiled rare plants in Nevada

Introduction

Rare plant species comprise >36.5% of the world’s flora and disproportionately support ecosystem function and resilience. However, rare species also lead global plant extinctions, and unique ecological characteristics can make them vulnerable to anthropogenic pressure. Despite their vulnerability, many rare plants receive less monitoring than is needed to inform conservation efforts due to limited capacity for field surveys.

Methods

We used field observations and geospatial data to summarize how 128 imperiled, rare vascular plant species in Nevada are affected by various threats. We assessed correlations between threats predicted by geospatial data and threats observed on the ground and asked how historic and current threats compare.

Results

The most commonly observed threats were from recreation, invasive and non-native/alien species, and livestock farming and ranching. Threat prevalence varied by elevation (e.g., a greater variety of threats at lower elevations, greater threat from climate change observed at higher elevations) and land management. There was a 28.1% overall correlation between predicted and observed threats, which was stronger for some threats (e.g., development of housing and urban areas, livestock farming and ranching) than others. All species experienced extreme climatic differences during 1990-2020 compared to baseline conditions, with the most extreme change in southern Nevada. The average number of threats observed per occurrence increased by 0.024 each decade.

Discussion

While geospatial data did not perfectly predict observed threats, many of these occurrences have not been visited in over 30 years, and correlations may be stronger than we were able to detect here. Our approach can be used to help guide proactive monitoring, conservation, and research efforts for vulnerable species.

Historical changes in plant water use and need in the continental United States

A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980-2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.

A unifying framework for studying and managing climate-driven rates of ecological change

During the Anthropocene and other eras of rapidly changing climates, rates of change of ecological systems can be described as fast, slow or abrupt. Fast ecological responses closely track climate change, slow responses substantively lag climate forcing, causing disequilibria and reduced fitness, and abrupt responses are characterized by nonlinear, threshold-type responses at rates that are large relative to background variability and forcing. All three kinds of climate-driven ecological dynamics are well documented in contemporary studies, palaeoecology and invasion biology. This fast-slow-abrupt conceptual framework helps unify a bifurcated climate-change literature, which tends to separately consider the ecological risks posed by slow or abrupt ecological dynamics. Given the prospect of ongoing climate change for the next several decades to centuries of the Anthropocene and wide variations in ecological rates of change, the theory and practice of managing ecological systems should shift attention from target states to target rates. A rates-focused framework broadens the strategic menu for managers to include options to both slow and accelerate ecological rates of change, seeks to reduce mismatch among climate and ecological rates of change, and provides a unified conceptual framework for tackling the distinct risks associated with fast, slow and abrupt ecological rates of change.

Seasonal weather and climate prediction over area burned in grasslands of northeast China

Grassland fire dynamics are subject to myriad climatic, biological, and anthropogenic drivers, thresholds, and feedbacks and therefore do not conform to assumptions of statistical stationarity. The presence of non-stationarity in time series data leads to ambiguous results that can misinform regional-level fire management strategies. This study employs non-stationarity in time series data among multiple variables and multiple intensities using dynamic simulations of autoregressive distributed lag models to elucidate key drivers of climate and ecological change on burned grasslands in Xilingol, China. We used unit root methods to select appropriate estimation methods for further analysis. Using the model estimations, we developed scenarios emulating the effects of instantaneous changes (i.e., shocks) of some significant variables on climate and ecological change. Changes in mean monthly wind speed and maximum temperature produce complex responses on area burned, directly, and through feedback relationships. Our framework addresses interactions among multiple drivers to explain fire and ecosystem responses in grasslands, and how these may be understood and prioritized in different empirical contexts needed to formulate effective fire management policies.