Climatic Predictors Research Articles

Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution.

The Plains Spotted Skunk (Spilogale interrupta) is a small carnivore native to central North America that has experienced significant population reductions, and there is a lack of information about the species that could inform conservation. Our study aimed to address knowledge gaps about the distribution and habitat associations of the species in South Dakota using species distribution modeling. We used species location data collected from state natural resource managers, trappers, and members of online social media groups dedicated to hunting and wildlife conservation; environmental predictors; and 6 predictive modeling algorithms (i.e., artificial neural networks, artificial classification tree analysis, generalized boosting models, maximum entropy, multivariate adaptive regression splines, and random forests) to develop climate and landcover ensemble distribution models. The most important climate and landcover predictors were mean temperature diurnal range (i.e., average monthly differences between daily high and low temperatures) and proportion of area classified as pasture. Ensemble model concordance identified approximately 31,300 km2 of potential Plains Spotted Skunk habitat primarily in eastern South Dakota and between the watersheds of the Missouri and James rivers. Our results offer insights that can guide conservation and inform effective management strategies for conserving Plains Spotted Skunk populations in the northern Great Plains. The promotion of low-intensity agricultural practices such as maintaining pastures, farm buildings, fences rows, and the management of woodland encroachment may improve habitat suitability and facilitate the recovery of plains spotted skunks in the region.

Models reveal shifting distribution of climatic suitability for pawpaw (Asimina triloba [L.] Dunal) cultivation under future climate change scenarios

The pawpaw (Asimina triloba [L.] Dunal) is a deciduous tree notable for its large edible fruit. Native to the eastern US and Canada, it has earned attention as a horticultural commodity and focus of scientific inquiry. However, few studies have modeled its potential future distribution under climate change. This study predicted the current and future potential distribution for pawpaw in North America and globally, with a focus on understanding future climatic suitability for fruit cultivation. This study first modeled suitability via the Maximum Entropy (MaxEnt) method by relating climate predictors with different datasets on pawpaw distribution, including nursery locations growing pawpaw. It also trained a boosted regression tree (BRT) model to estimate where sufficient heat accumulation for fruit ripening would occur. The models were applied to two future times (2041–2060 and 2081–2100), four emissions scenarios (SSP126, SSP245, SSP370, and SSP585), and climate projections from three climate models. Using nursery locations, the MaxEnt model yielded a mean area-under-the-curve statistic of 0.978 (standard deviation = 0.009) using 10-fold cross-validation, indicating strong predictive performance. The model suggested optimal conditions for pawpaw at these values: -4 °C for minimum temperature of coldest month, 26 °C for maximum temperature of warmest month, 88 cm for annual precipitation, and 0 % for precipitation seasonality. Models suggested shifting suitable climate conditions and accompanying increases in heat accumulation for fruit ripening. Northern America, Eastern Europe, and Northern Europe were predicted to have higher and increasing suitability; Western Europe, Southern Europe, and Eastern Asia were predicted to have higher but decreasing suitability. Little uncertainty existed due to collinearity shift or dissimilarity between current and future climate, but more uncertainty existed when predictions were based on differing climate model projections. This study provides insight into the pawpaw's potential response to climate change, and guidance on future locations for cultivation.

Classification Importance of Seed Morphology and Insights on Large-Scale Climate-Driven Strophiole Size Changes in the Iberian Endemic Chasmophytic Genus Petrocoptis (Caryophyllaceae).

Recruitment poses significant challenges for narrow endemic plant species inhabiting extreme environments like vertical cliffs. Investigating seed traits in these plants is crucial for understanding the adaptive properties of chasmophytes. Focusing on the Iberian endemic genus Petrocoptis A. Braun ex Endl., a strophiole-bearing Caryophyllaceae, this study explored the relationships between seed traits and climatic variables, aiming to shed light on the strophiole's biological role and assess its classificatory power. We analysed 2773 seeds (557 individuals) from 84 populations spanning the genus' entire distribution range. Employing cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability. Linear mixed-effects models were utilized to investigate the relationship between climate predictors and strophiole area, seed area and the ratio between both. The combination of seed morphometric traits allows the division of the genus into three well-defined morphogroups. The subsequent validation of the algorithm allowed 87% of the seeds to be correctly classified. Part of the intra- and interpopulation variability found in strophiole raw and relative size could be explained by average annual rainfall and average annual maximum temperature. Strophiole size in Petrocoptis could have been potentially driven by adaptation to local climates through the investment of more resources in the production of bigger strophioles to increase the hydration ability of the seed in dry and warm climates. This reinforces the idea of the strophiole being involved in seed water uptake and germination regulation in Petrocoptis. Similar relationships have not been previously reported for strophioles or other analogous structures in Angiosperms.

FORECASTING LEAFY VEGETABLE PRODUCTION IN BANGLADESH: UNRAVELLING THE NEXUS AMONG AGRO-METEOROLOGICAL FACTORS

Leafy vegetables in the food basket provide vital nutrients required for human health, but Bangladesh faces challenges of sustainable production potential due to the vulnerability of various agrometeorological factors. This study has addressed a holistic approach to forecasting leafy vegetable production through the nexus of agricultural responses and climatic factors. The secondary yearly data of seasonal leafy vegetable production and area wise daily key climatic predictors such as rainfall, humidity, sunshine, and temperature for the period 2000-2001 to 2022-2023 have been collected from Bangladesh Bureau of Statistics (BBS) and Bangladesh Meteorological Division (BMD), respectively. Scenario analysis and stepwise dynamic regression model was applied to diagnose the pattern and also forecast winter and summer leafy vegetable production for the years 2023-2024 to 2030-2031 addressing meteorological predictors. Results show that winter production is significantly influenced by the previous year’s output (lag-1), sunshine hour, and maximum temperature. In contrast, summer leafy vegetable production is influenced solely by previous year production. Using growth projection of sunshine hours and maximum temperature of Bangladesh Meteorological Department (BMD) in the fitted dynamic regression model, winter leafy vegetable production is forecasted to rise from 167,558 tons in 2022-2023 to 211,929 tons by 2030-2031. Summer production is expected to increase from 176,234 tons in 2022-2023 to 285,085 tons by 2030-2031. These findings provide critical insights for policymakers aiming to enhance food security and stabilize leafy vegetable markets amidst climate change for achieving sustainable development goals (SDG) 12.

The joint effect of changes in urbanization and climate on trends in floods: A comparison of panel and single-station quantile regression approaches

Estimates of annual maximum (peak) flow quantiles are needed for basins undergoing changes in both urbanization and climate. Most previous work on the effect of urbanization on peak flows has considered urbanization alone and only the spatial variation in flood quantiles or its mean temporal effect, and most work on the effect of nonstationarity in climate has focused on single-station analyses, which give uncertain results for extreme quantiles. To address these gaps, three approaches to the statistical estimation of the joint effects of changes in impervious cover and climate on the estimation of peak-flow quantiles were compared: single-station quantile regression; a fixed effect panel-quantile regression (pQR) method using a location (mean) shift to homogenize the panel; and a location-scale panel regression model (pQRmom), which accounts for both scale (variance) and location effects. The different approaches were applied to a dataset consisting of instantaneous annual peak flows from 127 minimally nested basins in the midwestern United States with at least 4 % change in imperviousness. The annual maximum daily discharge from a water-balance model was selected as the primary climate predictor; in addition, to provide a comparison of climate predictors, precipitation was also considered. The coefficients from single-station regressions were usually sufficiently certain to determine the effects of climate variation but usually too uncertain to estimate the effects of urbanization. The panel-quantile regression approaches give much more certain results, but their estimates of quantile dependence differ: although both indicate urbanization effects decreasing with decreasing annual exceedance probability (AEP), the pQRmom urbanization coefficients are insignificantly different from zero for AEPs less than 0.10, whereas the pQR coefficients remain positive and are significant except for AEP = 0.01, the smallest AEP value considered. Although the location-scale structure of the pQRmom approach has less flexible quantile dependence than the pQR approach, the pQRmom approach has somewhat lower overall error, and it is found that by subsetting the dataset to homogenize the scale effects, the pQR and pQRmom results become similar, indicating the insignificant urbanization coefficients for small AEPs of the pQRmom results are likely correct for the study dataset.

A Multi-Scale Species Distribution Model for Migrating and Overwintering Western Monarch Butterflies: Climate Is the Best Predictor

Western Monarch butterflies (Danaus plexippus) migrate from inland breeding ranges to coastal overwintering grounds in California. Given that migratory individuals may make multi-scale habitat selection decisions, we considered a multi-scale species distribution model (SDM) using range-wide climatic and local landscape-level predictors of migratory and overwintering habitat and community-science presence data. The range-wide model output was included as a predictor in the local-scale model, generating multi-scale habitat suitability. The top range-wide predictor was the minimum temperature in December, contributing 83.7% to the model, and was positively associated with presence. At the local scale, the strongest predictors of presence were the range-wide output and percent coverage of low and medium levels of development, contributing > 95%, with 61–63% from the range-wide output, with local-scale suitability coinciding with the California coastal zones. Development’s positive association with overwintering monarch presence was counterintuitive. It is likely that our local-scale model is overfit to these development zones, but it is unclear whether this overfitting resulted from modeler choices, monarchs overwintering close to human development, biased detection near human development, or a combination of these factors. Therefore, alternative approaches to collecting local-scale attribute data are suggested while recognizing the primacy of climate in restricting overwinter sites.

A Host Tree and Its Specialist Insects: Black Locust (Robinia pseudoacacia) Availability Largely Determines the Future Range Dynamics of Its Specialist Insects in Europe.

Black locust is the only host of Robinia-specialist insects in Europe. However, no study to date has examined future range shifts of specialist insects, and the relative effects of host plant availability and other factors on their range shifts. Here, we characterized the future range shifts in the host and its four specialist insects and the factors contributing to changes in their ranges. We detected substantial range expansions in all target species. Climate predictors and host plant availability were expected to have the strongest effects on the range shifts in the host and its specialist insects, respectively, suggesting that the specialist insects will track the ranges of their host. Parectopa robiniella showed the largest potential and expanding ranges and should be made a priority species for controlling invasions of Robinia-specialist insects in Europe. The expanding ranges of all specialist species were largely identified in the United Kingdom, Germany, and France, suggesting that these should be priority regions for mitigating their effects on ecosystems. Reducing future climate change is essential for preventing the spreading of specialist insects in Europe since specialist insects track their specialist host plants, and host range expansions are mainly driven by future climate changes.

Antecedent Effect Models as an Exploratory Tool to Link Climate Drivers to Herbaceous Perennial Population Dynamics Data

ABSTRACTUnderstanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool. We compare the predictive performance of antecedent effect models against simpler models and showcase their exploratory analysis potential by selecting a case study with high predictive power. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish horseshoe model (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between seven and 36 years in length. We then fit models to the asymptotic population growth rate (λ) and its underlying vital rates: survival, development, and reproduction. We find that models including climate do not consistently outperform null models. We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. We conclude that, in temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools for hypothesis generation.

Predicted future range expansion of a small carnivore: swift fox in North America

ContextSmall carnivores are declining globally due to a complex suite of threats. Conservation of these species requires an understanding of their distributions and potential responses to future land-use and climate change.ObjectivesWe modelled species-environment relationships of swift fox (Vulpes velox), a species of concern across their range. We developed spatial projections of current and future distribution to aid in conservation planning.MethodsWe assembled swift fox occurrence data from managers and community science sources to develop ensemble distribution models. In addition to landscape and climatic predictors, we developed a model of red fox distribution to represent effects of competition. We forecasted spatial predictions into the year 2070 under two climate change scenarios representing high (SSP 5–8.5) and low (SSP 1–2.6) emissions scenarios.ResultsPercent cover by grassland, mean annual precipitation, and minimum temperature of the coldest quarter were the three most important variables for swift fox distribution. Current suitable habitat for swift fox extends across 16 North American states and provinces. Future projections of swift fox distribution suggest an overall increase in area of swift fox suitable habitat under both emissions scenarios of > 56.9%, though patterns of gain and loss vary spatially.ConclusionsThe expansion of suitable habitat in future scenarios reflects swift fox adaptability to multiple land uses in a period following multi-organizational conservation efforts. Our spatial projections can be used in conservation planning and can serve as a case study of a small carnivore species likely to recover under future change scenarios provided that threats are addressed and landscape-scale conservation efforts continue.

Machine learning ensemble model prediction of northward shift in potato cyst nematodes (Globodera rostochiensis and G. pallida) distribution under climate change conditions

Potato cyst nematodes (PCNs) are a significant threat to potato production, having caused substantial damage in many countries. Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies, especially given the impact of climate change on pest species invasion and distribution. Machine learning (ML), specifically ensemble models, has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets. Thus, this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions, providing the initial element for invasion risk assessment. We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors. Then, five machine learning models were employed to build two groups of ensembles, single-algorithm ensembles (ESA) and multi-algorithm ensembles (EMA), and compared their performances. In this research, the EMA did not always perform better than the ESA, and the ESA of Artificial Neural Network gave the highest performance while being cost-effective. Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes. However, the total area of suitable regions will not change significantly, occupying 16–20% of the total land surface (18% under current conditions). This research alerts policymakers and practitioners to the risk of PCNs’ incursion into new regions. Additionally, this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.

Why did the Hornbill not cross the river? Upland habitats rather than a physical barrier limit the distribution of the Brown Hornbill

AbstractAimIn continuous environments, niche limits of species often determine their distribution limits. However, when these limits spatially coincide with a perceived dispersal barrier, the determinants of species' ranges may be confounded. We investigate the distribution pattern of the Brown Hornbill (Aves: Bucerotidae), which spans significant riverine barriers, but stops south of the Brahmaputra River. Considering its preference for low‐elevation evergreen forests, we posit that the lack of sufficient habitats north of the Brahmaputra prevents dispersal of the Brown Hornbill, and not the river itself.LocationThe Brahmaputra valley and the Indo‐Burma hotspot.TaxaAnorrhinus austeni, Aceros nipalensis.MethodsWe analysed citizen‐science occurrence data on hornbill presence against a suite of climatic, canopy‐specific and topographical predictors to model the environmental niche of the Brown Hornbill. We used presence‐only maximum entropy modelling in an information theoretic framework, in conjunction with constructing binary logistic regression models using presence and pseudoabsence data. We compared niche models of the Brown Hornbill and the Rufous‐necked hornbill, a close relative with a similar distribution, but which has spanned the Brahmaputra River.ResultsDespite the presence of suitable wet‐evergreen forests north of the Brahmaputra River, the hilly terrains in the region act as a biogeographic barrier for the Brown Hornbill, which prefers lowland evergreen forests. Further, highly suitable regions for the Brown and the Rufous‐necked Hornbills precisely delineate low and high‐elevation evergreen forests respectively, indicating that these birds are separated along an elevational axis.Main ConclusionsThe Brahmaputra River lies at the cusp of two subtly different environmental regimes. It may therefore serve as the niche limit for certain organisms and not as a physical obstacle to their dispersal. Our study implicitly predicts how widespread deforestation prevalent in the lowland evergreen forests of this region adversely impacts the distributions of organisms that depend on them, such as the Brown Hornbill. Further, our study proffers an approach to ascertain determinants of species distributions in a hypothesis testing framework.

The future of plant diversity within a Mediterranean endemism centre: Modelling the synergistic effects of climate and land-use change in Peloponnese, Greece

Climate- and land-use change stand as primary threats to terrestrial biodiversity. Yet, their synergistic impacts on species distributions remain poorly understood. To address this knowledge gap, we conducted the first-ever comprehensive species distribution analysis on an entire regional endemism centre within an eastern Mediterranean country, incorporating dynamic land-use/land-cover change data together with climate change scenarios. Specifically, we apply species distribution modelling and spatial data analysis techniques to compare the individual and synergistic effects of these environmental drivers on the endemic vascular flora of Peloponnese, focusing on potential range contractions, altitudinal shifts, and habitat fragmentation levels. Moreover, we identify fine-scale present and potential future endemism hotspots within our study area, incorporating taxonomic and phylogenetic information. Overall, we aim to enhance our current understanding of endemism patterns and contribute to the development of future-proof conservation strategies for safeguarding Greece's endangered endemic flora. The integration of land-use change projections with climate change yielded less severe impacts compared to the effects anticipated when considering climatic variables alone. Most taxa are expected to undergo significant range declines and nearly half might experience increased habitat fragmentation, due to the synergistic effects of climate- and land-use change. We identified endemism hotspots, which are concentrated in or along the main Peloponnesian mountain massifs. However, our predictions indicate that areas presently recognized as endemism hotspots will undergo a concerning area decline, across all future scenarios considered in this study. Our findings highlight the importance of including dynamic land-use variables alongside climatic predictors when projecting species distributions under global change. Moreover, we showed that endemism hotspots are not static and considering their potential geographic shifts is paramount to delineate effective forward-looking conservation strategies.

A new scenario free procedure to determine flood peak changes in the Harz Mountains in response to climate change projections

Study areaSix catchments of different hydrological characteristics in the Harz Mountains, Germany. Study focusA new scenario free method for determining changes of flood peaks considering climate change is developed. Compared to existing methods, it accounts for heavy rainfall changes by a newly developed factor with two seasons and establishes a numerical relationship to a greater number of relevant climate predictors. For easier application, it is based on frequently available daily measurements. A functional test of the factor for heavy rainfall changes and its adjustment algorithm for the precipitation time series is performed. Using a regional AR5 ensemble, for the first time the error and the uncertainty of a new scenario free method are estimated and compared with an existing method. The new method is applied in the Harz Mountains, where the sensitivity of the region to different climate predictors is investigated. New hydrological insights for the regionThe adjusting algorithm for precipitation is able to adjust the time series while maintaining mass balance. The new method has a lower error than the reference method, with better matches of changes in the median of the climate ensemble as well as the most ensemble members. In general, the uncertainty of the seasonal results is below the climate uncertainty of the AR5 ensemble. Regarding future flood peaks, the regional catchments are most sensitive to mean precipitation changes, followed by heavy rainfall changes especially in the winter season. Mean temperature changes are of minor significance, but the catchments characteristics are important. The new method can be recommended for assessments of climate change impacts on floods in low to average mountain regions in Germany and Europe.

Leaf area predicts conspecific spatial aggregation of woody species

AbstractAimAddressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co‐existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention.LocationGlobal synthesis.Time Period1997–2022.Major Taxa StudiedWoody angiosperms and conifers.MethodsWe carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat.ResultsWe hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observed aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza.Main ConclusionsCompared to climate variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area.

Big-brained alien birds tend to occur climatic niche shifts through enhanced behavioral innovation.

Identifying climatic niche shift and its influencing factors is of great significance in predicting the risk of alien species invasions accurately. Previous studies have attempted to identify the factors related to the niche shift of alien species in their invaded ranges, including changes in introduction history, selection of exact climate predictors, and anthropogenic factors. However, the effect of species-level traits on niche shift remains largely unexplored, especially those reflecting the species' adaptation ability to new environments. Based on the occurrence data of 117 successful alien bird invaders at a global scale, their native and invaded climatic niches were compared, and the potential influencing factors were identified. Our results show the niche overlap was low, with more than 75% of the non-native birds representing climatic niche shift (i.e. >10% niche expansion). In addition, 85% of the species showed a large proportion (mean ± SD, 39% ± 21%) of niche unfilling. Relative brain size (RBS) after accounting for body size had no direct effect on niche shift, but path analysis showed that RBS had an indirect effect on niche shift by acting on behavioral innovation primarily on technical innovation rather than consumer innovation. These findings suggested the incorporation of species' important behavioral adaptation traits may be promising to develop future prediction frameworks of biological invasion risk in response to the continued global change.

Are Bergmann's and Jordan's rules valid for a neotropical pitviper?

AbstractMorphological variation along the spatial distribution of species has been extensively investigated in ecological studies, and several ecogeographical rules explore the relationships between morphological traits and the environment. Many morphological traits are correlated, providing an opportunity to evaluate the validity of multiple ecogeographical rules simultaneously. Bergmann's rule predicts that endothermic animals in colder locations are larger than those in warmer locations. Jordan's rule predicts that fish from colder locations have more vertebrae than those from warmer locations. We tested the validity of Bergmann's and Jordan's rules for the neotropical lancehead snake Bothrops jararaca. We evaluated three morphological characters of 342 specimens: number of ventral scales (proxy for vertebrae number), snout–vent length (a linear measure of body size) and stoutness (volumetric body size). We implemented spatial regressions to evaluate the variation of morphological dimensions using climatic predictors: the minimum temperature and evapotranspiration. SVL was poorly related to minimum temperature and evapotranspiration. However, stouter individuals were found in colder places with greater evapotranspiration, following Bergmann's rule and the water conservation hypothesis. Individuals in warmer locations also had a greater number of ventral scales, reversing Jordan's rule. We showed that different selective pressures act on different morphological dimensions. Although stoutness follows Bergmann's rule, its variation would arise from an energy storage demand rather than heat conservation. Also, stoutness variation along evapotranspiration gradients could represent a mechanism to avoid hydric stress in environments with considerable climatic variations. The variation in vertebrae number along temperature gradients could be related to ecological factors and foraging. We highlight that physioecological mechanisms to deal with climatic variation and ecological aspects could be identified in snakes through intraspecific analyses, contrasting with interspecific studies that can hardly detect general trends. Due to different environmental effects on body size, we shed new light on the importance of exploring multiple morphological dimensions in macroecological studies.

Gaps and opportunities in modelling human influence on species distributions in the Anthropocene

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no ‘rule of thumb’ for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts

Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM10) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-model ensemble (MME). We identified potential climate predictors that potentially affect the wintertime air quality variability in South Korea in the global domain. From these potential climate predictors, those that can be forecasted skillfully by APCC MME were utilized to establish a multiple-linear regression model to predict the winter PM10 concentration in South Korea. As a result of evaluating the forecast skill through retrospective forecasts for the past 25 winters (1995/96-2019/20), this model showed statistically significant forecast skill at a lead time of a month to a season. The skill of PM10 forecast from the MME was overall better than that from a single model. We also found that it is possible to improve forecast skills through optimal MME combinations.

Rain use efficiency gradients across Australian ecosystems

Rain use efficiency (RUE) quantifies the ecosystem's capacity to use precipitation water to assimilate atmospheric CO2. The spatial distribution of RUE and its drivers across the Australian continent is largely unknown. This knowledge gap limits our understanding of the possible contribution of Australian ecosystems to global carbon assimilation. This study investigates the spatial distribution of RUE across diverse terrestrial ecosystems in Australia. The results show that RUE ranges from 0.43 (1st percentile) to 3.10 (99th percentile) g C m−2 mm−1 with a continental mean of 1.19 g C m−2 mm−1. About 68 % of the spatiotemporal variability of RUE can be explained by a multiple linear regression model primarily contributed by climatic predictors. Benchmarked by the model estimation, drainage-diverging/converging landscapes tend to have reduced/increased RUE. The model also revealed the impact of increasing atmospheric CO2 concentration on RUE. The continental mean RUE would increase by between 29.3 and 64.8 % by the end of this century under the SSP5–8.5 scenario in which the CO2 concentration is projected to double from the present level. This increase in projected RUE is attributed to the assumed greening effect of increasing CO2 concentration, which does not consider the saturation of CO2 fertilisation effect and the warming effect on increasing wildfire occurrence. Under the SSP1–2.6 scenario, RUE would decrease by about 7 %. This study provides baseline RUEs of various ecosystems in Australia for investigating the impacts of human interferences and climate change on the capacity of Australian vegetation to assimilate atmospheric CO2 under given precipitation.

Longitudinal investigation of restructuring, psychosocial safety climate and burnout in Australian universities during COVID-19 2020–2022

Australian universities have undergone considerable restructuring within the last few decades such as downsizing, unit amalgamations and adopting new digital work practices. This article draws together industrial relations and organisational psychology literature, exploring restructuring and effects within universities. It presents national data on university restructuring, levels of organisational psychosocial safety climate (PSC), and burnout from 2020 to 2022, from the workers’ perspective. Survey responses were collected across 39 Australian universities at 3 time points: 2020 (n = 2191), 2021 (n = 1731) and 2022 (n = 1373). A large proportion reported ‘high’ levels of restructuring at each data collection phase (2020 = 41%, 2021 = 56% and 2022 = 49%). A multi-level model showed that PSC is an organisational climate predictor of restructuring, and in turn, worker burnout. Hierarchical linear modelling of the longitudinal data revealed significant pathways and a good model fit. Findings suggest that organisations with poor climates for psychological health (i.e. low PSC) are more likely to restructure, which is then associated with higher levels of burnout. Primary-level PSC-focused interventions are needed to prioritise the psychological health of the workforce over persistent productivity concerns, which is the conventional driver of restructuring.