Climatic Drivers Research Articles

Climatic drivers and tree growth in a key production species: The case of Cryptomeria japonica (Thunb. ex L.f.) D.Don in the Azores archipelago

Forest management plays a crucial role in preserving and enhancing the delivery of ecosystem services, whereas dendrochronological methods can play a significant role in this regard. In the Azores archipelago, with a temperate oceanic climate, with low thermal amplitude, and mild and relatively wet Summers, Cryptomeria japonica (Thunb. ex L.f.) D.Don is the main timber production species. It has been extensively planted for timber production and erosion control, being the mainstay of the regional forestry chain, with a rotation period of 30 years. Although dendrochronological studies have targeted this species elsewhere, this dendrochronological study in the Azores aimed to better understand its climate-growth relationships in the archipelago. For this purpose, we sampled 140 trees, in a total of 361 wood cores samples. Following standard dendrochronological methods, we obtained four site chronologies from different volcanic complexes in São Miguel island. We used a stepwise modelling approach, with Random Forest, Principal Component Analysis and Generalized Linear Models. Our results suggest that despite year-round precipitation in the Azores, C. japonica is adversely affected by warm Winter and Summers, while on site SC it benefits from these conditions. As previously observed, precipitation may be overshadowed by temperature-driven mechanisms in C. japonica. Our spatial analysis resulted into climate-growth associations with similar directions for all site chronologies. In the future, higher Winters and Summer temperatures could lead to increased water stress and reduced growth rates. This should be considered when projecting the future distribution and productivity of C. japonica forests under different climate change scenarios. To ensure the long-term survival of this economically important tree species, adaptation responses should include genetic and conservation measures. Our findings provide baseline information for defining management approaches for this strategic species.

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

Thermal Response of Large Seasonally Ice‐Covered Lakes Over Tibetan Plateau to Climate Change

AbstractIn this study, a process‐based lake model is used to investigate the influence of climate change on the thermodynamics of 30 large lakes over Tibetan Plateau (TP). The lake model was driven by the atmospheric forcing derived from the bias‐corrected projections of three global climate models in the twenty‐first century under three Shared Socioeconomic Pathways (SSPs). The hindcasts during 2000–2014 can reasonably capture the seasonality and magnitude of satellite retrieved lake surface temperature (LST). Future projections during 2015–2100 suggest a widespread increased LST, declined ice cover, and prolonged stratification, with the severity of changes in line with the climate driver shifts under different SSPs. Under the scenario with the highest level of anthropogenic radiative forcing (SSP5‐8.5), the end‐of‐century (2086–2100) changes of LST, ice thickness, and stratification duration averaged across all studied lakes reach 4.90°C, −0.43 m, and 65.46 days, respectively. Note that the positive ice‐albedo feedback can cause excess lake warming by accelerating ice break‐up (30.07 days earlier) and stratification onset (46.83 days earlier). By the end of this century, more frequent, multi‐seasonal thermal extremes are anticipated to push nearly half of the studied lakes into a permanent heatwave state. Together with the remarkable LST increase and winter ice loss, the lakes will mix less frequently and may shift from a dimictic to warm monomict mixing regime. Hopefully, the irreversible thermal changes can be avoided if the anthropogenic radiative forcing is controlled within the envelope outlined by the stringent climate mitigation scenario SSP1‐2.6.

Contraction and expansion: global geographical variation in reproductive systems of Primula is driven by different mechanisms

AbstractAimReproductive systems strongly influence plants' evolution and adaption, and the biogeographic pattern of its variation has intrigued biologists and ecologists. Here, to test the impacts of paleoglaciation on plants' reproductive system variation, we compared the geographical pattern and environmental drivers in the proportions of different reproductive systems in Primula in regions affected and unaffected by paleoglaciation.LocationGlobal.Time PeriodSince the Last Glacial Maximum (LGM).Major Taxa Studied Primula. MethodsUsing data on reproductive systems and distributions of 604 Primula taxa around the world and 221 along the elevational gradient of the Himalayas, we demonstrated the global pattern and the elevational pattern in the proportions of homostylous taxa. We employed general linear models to establish the relationship between the proportion of homostylous taxa and environmental variables and hierarchical partitioning to assess the relative contributions of these variables in both regions affected and unaffected by paleoglaciation, respectively.ResultsWe found a higher proportion of homostylous taxa in regions glaciated during the LGM than those unglaciated, with different latitudinal patterns and climate drivers. The proportion of homostylous taxa showed varying trends across different regions: increasing with latitude and temperature anomaly in glaciated regions while concentrating at lower latitudes with higher winter temperatures in unglaciated regions. Additionally, homostylous taxa were more prevalent at lower elevations in the Himalayas.Main ConclusionsOur study provides the first quantitative evidence for the hypothesis that selfers are more prevailing in regions affected by paleoglaciation facilitated by recolonization via comparing the geographic pattern and drivers in regions affected and unaffected by paleoglaciation. Our findings also reveal the concentrated distribution of homostylous taxa in Primula at low latitudes, which may be the result of population shrinkage caused by heat stress, facing a more severe survival crisis under the circumstances of global warming and increasing human activities.

Changes in moisture sources contributed to the onset and development of the 2017-2019 southeast Australian drought

In 2017-2019 southeast Australia experienced one of its most severe droughts since 1900. Rainfall over the region encompassing the Murray-Darling Basin was consistently below average for three consecutive cool seasons, an unprecedented event on record. A strong positive Indian Ocean Dipole event has been previously suggested to have intensified the conditions of the drought in 2019, however the state of the climate drivers cannot fully explain the onset and development of this drought. In this study, we adopt a different approach to investigate processes other than remote climate drivers that may have triggered the drought. Using a Lagrangian model to identify moisture sources to the Murray Darling Basin, we show that local processes were crucial in explaining the onset and development of the drought. We identify the oceanic and terrestrial sources of atmospheric moisture over the Murray Darling Basin and show for the first time a significant decline in rainfall moisture supply from the Tasman Sea in 2017 and 2018. We further show that anomalous atmospheric circulation transported the expected moisture northward toward the Maritime Continent. Our results provide an explanation for the moisture and rainfall deficit that caused the 2017-19 southeast Australian drought. Understanding the processes that led to the 2017-2019 drought is an important step towards improved predictions and planning for future multi-year droughts in Australia.

Variation of the Start Date of the Vegetation Growing Season (SOS) and Its Climatic Drivers in the Tibetan Plateau.

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY-170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.

A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia

The increasing frequency and duration of severe fire events in Australia further necessitate accurate and timely forecasting to mitigate their consequences. This study evaluated the performance of two distinct approaches to forecasting extreme fire danger at two- to three-week lead times for the period 2003 to 2017: the official Australian climate simulation dynamical model and a statistical model based on climate drivers. We employed linear logistic regression to develop the statistical model, assessing the influence of individual climate drivers using single linear regression. The performance of both models was evaluated through case studies of three significant extreme fire events in Australia: the Canberra (2003), Black Saturday (2009), and Pinery (2015) fires. The results revealed that ACCESS-S2 generally underestimated the spatial extent of all three extreme FBI events, but with accuracy scores ranging from 0.66 to 0.86 across the case studies. Conversely, the statistical model tended to overpredict the area affected by extreme FBI, with high false alarm ratios between 0.44 and 0.66. However, the statistical model demonstrated higher probability of detection scores, ranging from 0.57 to 0.87 compared with 0.03 to 0.57 for the dynamic model. These findings highlight the complementary strengths and limitations of both forecasting approaches. Integrating dynamical and statistical models with transparent communication of their uncertainties could potentially improve accuracy and reduce false alarms. This can be achieved through hybrid forecasting, combined with visual inspection and comparison between the statistical and dynamical forecasts. Hybrid forecasting also has the potential to increase forecast lead times to up to several months, ultimately aiding in decision-making and resource allocation for fire management.

Urbanization vs. climate drivers: investigating changes in fluvial floods in Poland

Urbanization vs. climate drivers: investigating changes in fluvial floods in Poland

Spatiotemporal pattern of NPP and its response to climatic factors in the Yangtze River Economic Belt

Net primary productivity (NPP) of terrestrial ecosystems serves as a crucial carbon sink, playing a significant role in mitigating climate change. This study, focusing on the Yangtze River Economic Belt (YREB), a key area for both economic development and ecological conservation in China, evaluates the temporal and spatial changes in regional NPP and its responses to climatic factors against the backdrop of global change. Utilizing long-term NPP and meteorological grid data from 1981 to 2017, we analyzed the annual change patterns of NPP and its linear and nonlinear responses to climatic factors using copula functions. Our findings indicate that 57.00 % of the YREB has experienced a significant increase in NPP, with the most substantial decreases observed in Shanghai and its surrounding areas (P < 0.05). Temperature emerged as the primary climatic driver, positively correlating with NPP changes in over half of the study area (P < 0.05). Moreover, the nonlinear response of NPP to temperature and precipitation underscores the complex interactions between these factors, revealing a sensitivity to temperature variations that surpasses that to precipitation. The study also highlights the influence of solar radiation and topographic factors, with a significant positive correlation between NPP and solar radiation observed in 15.85 % of the area (P < 0.05). The practical significance of these findings lies in their implications for regional ecological management and climate resilience strategies. The positive correlation between temperature and NPP underscores the potential for targeted temperature management to optimize carbon sequestration across the YREB. Furthermore, the identified nonlinear responses and the role of solar radiation and topography in influencing NPP offer valuable insights for the formulation of adaptive strategies that enhance ecosystem productivity and contribute to achieving carbon neutrality goals. By elucidating the dynamics of NPP in response to a range of environmental factors, this study provides a foundation for informed decision-making in ecological conservation and climate change mitigation efforts within the YREB and similar regions globally.

Machine Learning Identification of Attributes and Predictors for a Flash Drought in Eastern Australia

Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of the Upper Hunter region of Eastern Australia, an important energy and agricultural local and global exporter that is both flood- and drought-prone. Here, the authors investigate the FD that started abruptly in May 2023 and extended to October 2023. The FD followed floods in November 2021 and much above-average May–October 2022 rainfall. Eight machine learning (ML) regression techniques were applied to the 60 May–October periods from 1963–2022, using a rolling windows attribution search from 45 possible climate drivers, both individually and in combination. The six most prominent climate drivers, and likely predictors, provide an understanding of the major contributors to the FD. Next, the 1963–2022 data were divided into two shorter timespans, 1963–1992 and 1993–2022, generally accepted as representing the early and accelerated global warming periods, respectively. The key attributes were markedly different for the two timespans. These differences are readily explained by the impacts of global warming on hemispheric and synoptic-scale atmospheric circulations.

The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features

In 2023 Amazonia experienced both historical drought and warm conditions. On October 26th 2023 the water levels at the port of Manaus reached its lowest record since 1902 (12.70 m). In this region, October monthly maximum and minimum temperature anomalies also surpassed previous record values registered in 2015 (+ 3 °C above the normal considering the 1981–2020 average). Here we show that this historical dry and warm situation in Amazonia is associated with two main atmospheric mechanisms: (i) the November 2022–February 2023 southern anomaly of vertical integrated moisture flux (VIMF), related to VIMF divergence and extreme rainfall deficit over southwestern Amazonia, and (ii) the June–August 2023 downward motion over northern Amazonia related to extreme rainfall deficit and warm conditions over this region. Anomalies of both atmospheric mechanisms reached record values during this event. The first mechanism is significantly correlated to negative sea surface temperature (SST) anomalies in the equatorial Pacific (November–February La Niña events). The second mechanism is significantly correlated to positive SST anomalies in the equatorial Pacific, related to the impacts of June–September El Niño on the Walker Circulation. While previous extreme droughts were linked to El Niño (warmer North Tropical Atlantic SST) during the austral summer (winter and spring), the transition from La Niña 2022–23 to El Niño 2023 appears to be a key climatic driver in this record-breaking dry and warm situation, combined to a widespread anomalous warming over the worldwide ocean.

Metabolic niches in the rhizosphere microbiome: dependence on soil horizons, root traits and climate variables in forest ecosystems.

Understanding belowground plant-microbial interactions is important for biodiversity maintenance, community assembly and ecosystem functioning of forest ecosystems. Consequently, a large number of studies were conducted on root and microbial interactions, especially in the context of precipitation and temperature gradients under global climate change scenarios. Forests ecosystems have high biodiversity of plants and associated microbes, and contribute to major primary productivity of terrestrial ecosystems. However, the impact of root metabolites/exudates and root traits on soil microbial functional groups along these climate gradients is poorly described in these forest ecosystems. The plant root system exhibits differentiated exudation profiles and considerable trait plasticity in terms of root morphological/phenotypic traits, which can cause shifts in microbial abundance and diversity. The root metabolites composed of primary and secondary metabolites and volatile organic compounds that have diverse roles in appealing to and preventing distinct microbial strains, thus benefit plant fitness and growth, and tolerance to abiotic stresses such as drought. Climatic factors significantly alter the quantity and quality of metabolites that forest trees secrete into the soil. Thus, the heterogeneities in the rhizosphere due to different climate drivers generate ecological niches for various microbial assemblages to foster beneficial rhizospheric interactions in the forest ecosystems. However, the root exudations and microbial diversity in forest trees vary across different soil layers due to alterations in root system architecture, soil moisture, temperature, and nutrient stoichiometry. Changes in root system architecture or traits, e.g. root tissue density (RTD), specific root length (SRL), and specific root area (SRA), impact the root exudation profile and amount released into the soil and thus influence the abundance and diversity of different functional guilds of microbes. Here, we review the current knowledge about root morphological and functional (root exudation) trait changes that affect microbial interactions along drought and temperature gradients. This review aims to clarify how forest trees adapt to challenging environments by leveraging their root traits to interact beneficially with microbes. Understanding these strategies is vital for comprehending plant adaptation under global climate change, with significant implications for future research in plant biodiversity conservation, particularly within forest ecosystems.

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p &lt; 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

Assessing scale-dependency of climate risks in coffee-based agroforestry systems

Agroforestry is a management strategy for mitigating the negative impacts of climate and adapting to sustainable farming systems. The successful implementation of agroforestry strategies requires that climate risks are appropriately assessed. The spatial scale, a critical determinant influencing climate impact assessments and, subsequently, agroforestry strategies, has been an overlooked dimension in the literature. In this study, climate risk impacts on robusta coffee production were investigated at different spatial scales in coffee-based agroforestry systems across India. Data from 314 coffee farms distributed across the districts of Chikmagalur and Coorg (Karnataka state) and Wayanad (Kerala state) were collected during the 2015/2016 to 2017/2018 coffee seasons and were used to quantify the key climate drivers of coffee yield. Projected climate data for two scenarios of change in global climate corresponding to (1) current baseline conditions (1985–2015) and (2) global mean temperatures 2 °C above preindustrial levels were then used to assess impacts on robusta coffee yield. Results indicated that at the district scale rainfall variability predominantly constrained coffee productivity, while at a broader regional scale, maximum temperature was the most important factor. Under a 2 °C global warming scenario relative to the baseline (1985–2015) climatic conditions, the changes in coffee yield exhibited spatial-scale dependent disparities. Whilst modest increases in yield (up to 5%) were projected from district-scale models, at the regional scale, reductions in coffee yield by 10–20% on average were found. These divergent impacts of climate risks underscore the imperative for coffee-based agroforestry systems to develop strategies that operate effectively at various scales to ensure better resilience to the changing climate.

Hidden impacts of ocean warming and acidification on biological responses of marine animals revealed through meta-analysis

Conflicting results remain on the impacts of climate change on marine organisms, hindering our capacity to predict the future state of marine ecosystems. To account for species-specific responses and for the ambiguous relation of most metrics to fitness, we develop a meta-analytical approach based on the deviation of responses from reference values (absolute change) to complement meta-analyses of directional (relative) changes in responses. Using this approach, we evaluate responses of fish and invertebrates to warming and acidification. We find that climate drivers induce directional changes in calcification, survival, and metabolism, and significant deviations in twice as many biological responses, including physiology, reproduction, behavior, and development. Widespread deviations of responses are detected even under moderate intensity levels of warming and acidification, while directional changes are mostly limited to more severe intensity levels. Because such deviations may result in ecological shifts impacting ecosystem structures and processes, our results suggest that climate change will likely have stronger impacts than those previously predicted based on directional changes alone.

Cropping decisions under water stress: Evidence from Cauvery Delta Region, India

AbstractCropping decisions under resource constraints such as water stress have a significant influence on the livelihoods of farm households. Using survey data from 152 farm households in three villages of the paddy producing zone in the Cauvery Delta Region in India, we examine the factors that determine selection of paddy varieties and cotton. Our findings show that the marketability of specific paddy varieties, given inadequate public procurement, and groundwater use, driven by reduced surface water flows, influence crop choice. Farmers have modified their sowing practices and cropping patterns as a response to water stress and market signals. We conclude that to develop effective interventions to improve farm‐based livelihoods, it is important to look not just at climatic drivers of crop choice and farm level adjustments but the multiple stressors at different scales.

Dust and climate interactions in the Middle East: Spatio-temporal analysis of aerosol optical depth and climatic variables

The Middle East (ME) is grappling with an alarming increase in dust levels, measured as aerosol optical depth (AOD), which poses significant threats to air quality, human health, and ecological stability. This study aimed to investigate correlations between climate and non-climate driving factors and AOD in the ME over the last four-decade (1980–2020), based on analysis of three variables: actual evapotranspiration (AET), potential evapotranspiration (PET), and precipitation (P). A comprehensive analysis is conducted to discern patterns and trends, with a particular focus on regions such as Rub al-Khali, Ad-Dahna, An-Nafud Desert, and southern Iraq, where consistently high dust levels were observed. 77 % of the study area is classified as arid or semi-arid based on the aridity index. Our results indicate an upward trend in dust levels in Iraq, Iran, Yemen, and Saudi Arabia. We noted an increasing AET trend in regions such as the Euphrates and Tigris basin, northern-Iran, and the Nile region, along with rising PET levels in arid and semi-arid zones such as Iran, Iraq, and Syria. Conversely, P showed a notable decrease in northern-Iraq, Syria, southwestern Iran, and southern-Turkey. Comparison of long-term changes (10-year moving averages) of AOD and P showed a consistent increase in AOD with P levels decreasing in all climate regions. The Budyko space analysis indicates shifts in evaporation ratio across different climate classes from 1980 to 2020, with predominant movement patterns towards higher aridity indices in arid and semi-arid regions, while factors beyond long-term aridity changes influence shifts in evaporation ratio across various climatic zones. The Middle East experiences complex and intricate interactions between dust events and their drivers. To address this issue, a comprehensive and multi-system approach is necessary, which considers both climate and non-climate drivers. Moreover, an efficient dust control strategy should include soil and water conservation, advanced monitoring, and public awareness campaigns that involve regional and international collaboration.

A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada

Throughout history, the pursuit of diagnosing and predicting crop yields has evidenced genetics, environment, and management practices intertwined in achieving food security. However, the sensitivity of crop phenotypes and genetic responses to climate still hampers the identification of the underlying abilities of plants to adapt to climate change. We hypothesize that the PiAnosi and WagNer (PAWN) global sensitivity analysis (GSA) coupled with a genetic by environment (GxE) model built of environmental covariance and genetic markers structures, can evidence the contributions of climate on the predictability of maize yields in the U.S. and Ontario, Canada. The GSA-GxE framework estimates the relative contribution of climate variables to improving maize yield predictions. Using an enhanced version of the Genomes to Fields initiative database, the GSA-GxE framework shows that the spatially aggregated sensitivity of maize yield predictability is attributed to solar radiation, followed by temperature, rainfall, and relative humidity. In one-third of the individually assessed locations, rainfall was the primary responsible for maize yield predictability. Also, a consistent pattern of top sensitivities (Relative Humidity, Solar Radiation, and Temperature) as the main or the second most relevant drivers of maize yield predictability shed some light on the drivers of genetic improvement in response to climate change.

Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

Societal Impact StatementAdapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships.Summary While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.

Climate drivers and human impacts shape 35‐year trends of coastal wetland health and composition in an urban region

AbstractThe future of coastal wetlands will depend on the combined effects of climate change and human impacts from urbanization and coastal management. Disentangling the effects of these factors is difficult, but satellite imagery archives provide a way to track biological and physical changes in wetlands over recent decades to reveal how coastal wetlands have been changing in response to climate and human drivers. In this study, we used Landsat to monitor the conditions of 32 coastal wetlands in southern California from 1984 to 2019 and identify environmental and human drivers of these trends. Wetland conditions were characterized by vegetation greenness, using the normalized difference vegetation index (NDVI), and by habitat composition, derived from areal estimates of wetland and subtidal habitats. Overall, wetlands displayed three types of long‐term response: greening and gaining wetland (10), greening and losing wetland (16), and browning and losing wetland (6). Regional environmental drivers with overall positive effects on wetland NDVI were sea level, wave height, and precipitation, whereas stream discharge, vapor pressure deficit, and air temperature had negative or nonlinear effects. Wetland area change was primarily correlated with sea level, but response was highly contextual among sites. Negative trends in wetland NDVI and area were more common in larger sites with low elevations and in sites with open inlets. Restoration had mixed effects, with only half of the restored sites showing positive changes in NDVI and wetland area post‐restoration. The important work of managing and restoring urban coastal wetlands is complicated by variability and context and requires us to account for the influence of humans and climate as we build a regional understanding of historic, present, and future wetland health.