Spatial Drivers Research Articles

Recent decline in suitable large mammal habitats within the Dzanga Sangha Protected Areas, Central African Republic

The forests of the Congo Basin are an important home to some of the world’s most critically endangered species, including the central chimpanzee (Pan troglodytes troglodytes), the western lowland gorilla (Gorilla gorilla gorilla) and the forest elephant (Loxodanta cyclotis). To contribute to the long-term sustainability of these species and their habitats within the Dzanga Sangha Protected Areas (DSPA) in the Central African Republic, the World Wide Fund for Nature (WWF) developed an ecological monitoring program to assess the spatial drivers of species' habitat changes. Here, we assess and quantify chimpanzee, gorilla and elephant habitat suitability within the DSPA using data from two survey years (2015 and 2020), to identify priority habitat areas and recommend conservation measures to mitigate ongoing habitat changes. We found that priority chimpanzee habitats covered about 1383 km2 (30 %) of the entire DSPA in the year 2015, while priority gorilla and elephant habitats covered approximately 2569 km2 (56 %) and 3075 km2 (67 %) respectively. Priority habitat area for the three species declined by 4, 4.5 % and 9.8 % points respectively between 2015 and 2020, mostly due to increased human pressures. We further provide evidence that the Dzanga National Park represents a region of higher priority habitat for all three species owing to the reduced human pressure that has resulted from higher eco-guard patrol efforts. Based on our analysis, we recommend maintaining a nonviolent patrol presence to mitigate human pressures within remaining priority habitat areas, recognizing also the importance of collaboration with local communities to support long-term conservation goals.

Urban growth, changing relationship between biophysical factors and surface thermal characteristics: A geospatial analysis of Agra city, India

The kind of urbanization occurred in the last few decades has caused a massive reduction of urban green space raising the land surface temperature, drastic land use and land cover changes, and hampering environmental sustainability in Agra city. Examining urban growth trends and its significant impacts on surface and vegetation cover of Agra helps in assessing nature of spatial development process. The present study examines urban growth, bio-physical factors and surface thermal characteristics in the city. Study uses multiple geospatial techniques to analyse changes. To obtain spatio-temporal landscape dynamics multi-temporal Landsat TM/OLI satellite images were used for years 1991, 2001, 2011 and 2021 to classify LULC classes using maximum likelihood supervised classification algorithm. Based on the spatial drivers and LULC of 2001 and 2011; multi perception based artificial neural network model is applied to predict the urban areas in 2031. Study reports that the greatest loss was found for vegetation cover (VC); a total of 17.89 sq. km. (13.06%) green space lost during the study periods (1991–2021). A remarkable increased in urban area was noticed at a rate of 11.20% during 1991–2001 and 7.97% during 2001–2011. The study has much to offer to urban and environmental planning and sustainable urbanization.

The Spatiotemporal Pattern and Driving Mechanism of Urban Sprawl in China’s Counties

Cities in China do not constitute a few global metropolises, but are characterized by heterogeneity. Studying counties can give us a comprehensive picture of urban sprawl in China. This study measured the sprawl index of 1880 counties in China from 2005 to 2020 for the first time and then revealed the evolution of their spatiotemporal characteristics and driving mechanisms. The results revealed the following. (1) China’s counties had a noticeable sprawling trend from 2005 to 2020, and their evolutionary process was characterized by spatiotemporal heterogeneity. (2) From 2005 to 2020, the counties’ sprawl gradually evolved into a spatial distribution pattern of high in the east and low in the west. The spatial distribution of sprawl in county and municipal districts had the characteristics of an interlocking distribution. (3) High–high cluster areas of CSI are mainly distributed in plains, and hilly, basin, and plateau areas tend to be low–low cluster areas. High–low outliers were distributed in a “point–line” pattern along the railroad lines and a cluster pattern near railroad intersections and central cities. Low–high outliers had the trend of encircling the high–high cluster areas. (4) The coefficient of the natural drivers was higher but tended to decrease, while the coefficient of economic and spatial drivers was lower but gradually increased. This study is the first to refine the study of urban sprawl to the county scale, which provides a reference for decision making to optimize the spatial structure of counties and thus promote high-quality development.

Spatial drivers and effects of urban food accessibility: Comparison of conventional and online-to-offline services

Nowadays, online-to-offline food delivery services have grown rapidly. However, the factors determining the spatial distribution of online-to-offline food delivery restaurants (O2O-FDR) and the effects (e.g., dietary health) they cause are poorly understood. This study explored the differences in spatial patterns and influencing factors between O2O-FDR and conventional restaurants (CR) accessibility in Nanjing, as well as the driving mechanisms for the food environment. The results show that (1) O2O-FDR accessibility and CR accessibility both have a centrally distributed pattern, while O2O-FDR accessibility is smaller and more dispersed. (2) O2O-FDR shows a clear economic orientation, with the regional economy (q = 0.48) having the strongest influence and interacting with the factor of distance to commercial center to influence 61% of the spatial distribution; while the distance to commercial centers is the dominant factor for CR accessibility (q = 0.53), and its interaction with the regional economy was the strongest (q = 0.66). (3) The proliferation of O2O-FDR in Nanjing is more consistent with the innovation diffusion hypothesis. (4) In comparison to CR, O2O-FDR has a stronger correlation with fast food restaurants accessibility, which potentially increases the availability of less healthy food products. This study contributes to urban planning and urban design that promotes healthy eating.

An improved spatial case-based reasoning considering multiple spatial drivers of geographic events and its application in landslide susceptibility mapping

Traditional case-based reasoning methods overlook non-stationary spatial drivers of geographical events such as heterogeneity, dependence, and accumulation in case representation, and directly obtain the solution of the most similar cases in case reuse instead of considering the interference of fake similar cases to eliminate the contingency of reasoning, which leads to poor interpretations and low efficiency decisions in complex and heterogeneous geographical environments. This study proposes an improved spatial case-based reasoning (SCBR) considering multiple spatial drivers to overcome above problems and uses landslide susceptibility mapping as an example. Specifically, these spatial drivers were captured, extracted, and integrated into case representation by using geographic self-organizing mapping algorithm, spatial statistic, and spatial adjacent matrix, respectively. Additionally, the K-nearest neighbor method as case retrieval was introduced to retrieve the K similar cases based on the local and global similarity reasoning. Finally, the Gaussian process regression as case reuse method was generated to landslide susceptibility index under the assumption that K similar cases follows Gaussian distribution. Our experimental results show that the precision, F1, recall, and kappa of the proposed SCBR method are 0.974, 0.976, 0.979, and 0.953 which are higher than those of the traditional case-based reasoning (0.931, 0.941, 0.953, and 0.881), long short-term memory (0.951, 0.933, 0.915, and 0.870), and extreme gradient boosting decision tree (0.963, 0.967, 0.972, and 0.945), respectively. In general, the novel approach with better predictive performance can help decision makers to develop policies that reduce the loess of landslides and apply to similar geological events.

Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models.

Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.

Environmental and topographic drivers of amphibian phylogenetic diversity and endemism in the Iberian Peninsula.

Understanding the ecological and evolutionary processes driving biodiversity patterns and allowing their persistence is of utmost importance. Many hypotheses have been proposed to explain spatial diversity patterns, including water-energy availability, habitat heterogeneity, and historical climatic refugia. The main goal of this study is to identify if general spatial drivers of species diversity patterns of phylogenetic diversity (PD) and phylogenetic endemism (PE) at the global scale are also predictive of PD and PE at regional scales, using Iberian amphibians as a case study. Our main hypothesis assumes that topography along with contemporary and historical climate are drivers of phylogenetic diversity and endemism, but that the strength of these predictors may be weaker at the regional scale than it tends to be at the global scale. We mapped spatial patterns of Iberian amphibians' phylogenetic diversity and endemism, using previously published phylogenetic and distribution data. Furthermore, we compiled spatial data on topographic and climatic variables related to the water-energy availability, topography, and historical climatic instability hypotheses. To test our hypotheses, we used Spatial Autoregressive Models and selected the best model to explain diversity patterns based on Akaike Information Criterion. Our results show that, out of the variables tested in our study, water-energy availability and historical climate instability are the most important drivers of amphibian diversity in Iberia. However, as predicted, the strength of these predictors in our case study is weaker than it tends to be at global scales. Thus, additional drivers should also be investigated and we suggest caution when interpreting these predictors as surrogates for different components of diversity.

The Spatial Drivers of Discrimination: Evidence From Anti-Muslim Fake News in India

The Spatial Drivers of Discrimination: Evidence From Anti-Muslim Fake News in India

Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model.

Urban vitality is a major indicator used for evaluating the sustainability and attractiveness of an urban environment. Global experience indicates that urban vitality can be stimulated through a reasonable urban design. However, it remains incompletely understood in the literature which building-related indicators can substantially affect urban vitality in Asian countries. To give an insight into this question, our study took a step forward by focusing specifically on the influence of the three-dimensional built environment on urban vitality, based on which decision makers could enhance urban vitality from the perspective of vertical building design. A machine-learning-based framework was developed in this study. First, we utilized several building-related indicators to thoroughly measure the spatial characteristics of buildings at the township level. Second, the relationship between a three-dimensional built environment and urban vitality was revealed based on a combined use of the correlation method, scatter charts, and a random forest. In the random forest, both a benchmark and a new model were constructed to evaluate the importance of those building-related indicators. The results suggested that urban vitality was closely related to the three-dimensional built environment, which played an even more important role than common benchmark factors in stimulating urban vitality. The building coverage ratio, density of tall buildings, and floor area ratio were essential spatial drivers behind urban vitality. Therefore, urban designers and decision makers should not only take traditional factors into account but also carefully consider the potential influence of high-rise buildings and the outdoor thermal environment so that urban vitality can be enhanced. Our study's results can offer practical recommendations for improving urban vitality from the perspective of vertical building design. The proposed framework can also be used for measuring the potential influence of the three-dimensional built environment in other areas.

Using satellite data to assess spatial drivers of bird diversity.

Birds are useful indicators of overall biodiversity, which continues to decline globally, despite targets to reduce its loss. The aim of this paper is to understand the importance of different spatial drivers for modelling bird distributions. Specifically, it assesses the importance of satellite-derived measures of habitat productivity, heterogeneity and landscape structure for modelling bird diversity across Great Britain. Random forest (RF) regression is used to assess the extent to which a combination of satellite-derived covariates explain woodland and farmland bird diversity and richness. Feature contribution analysis is then applied to assess the relationships between the response variable and the covariates in the final RF models. We show that much of the variation in farmland and woodland bird distributions is explained (R 2 0.64-0.77) using monthly habitat-specific productivity values and landscape structure (FRAGSTATS) metrics. The analysis highlights important spatial drivers of bird species richness and diversity, including high productivity grassland during spring for farmland birds and woodland patch edge length for woodland birds. The feature contribution provides insight into the form of the relationship between the spatial drivers and bird richness and diversity, including when a particular spatial driver affects bird richness positively or negatively. For example, for woodland bird diversity, the May 80th percentile Normalized Difference Vegetation Index (NDVI) for broadleaved woodland has a strong positive effect on bird richness when NDVI is >0.7 and a strong negative effect below. If relationships such as these are stable over time, they offer a useful analytical tool for understanding and comparing the influence of different spatial drivers.

Identifying individual and spatial drivers of heterogeneous transmission and virulence of malaria in Caribbean anoles

AbstractHeterogeneous distributions are a fundamental principle of ecology, manifesting as natural variability within ecological levels of organization from individuals to ecosystems. In disease ecology, variability in biotic and abiotic factors can result in heterogeneous patterns of transmission and virulence—broadly defined here as the negative consequences of infection. Still, our classic theoretical understanding of disease dynamics comes from models that assume homogeneous transmission and virulence. Here, we test this assumption by assessing the contribution of various sources of individual and spatial heterogeneity to patterns of transmission and sublethal measurements of virulence in two lizard–malaria systems: a three‐parasite assemblage (Plasmodium floridense,Plasmodium leukocytica, andPlasmodium azurophilum) infecting the lizardAnolis gundlachiin the rainforest of Puerto Rico and a single‐parasite system (P. floridense–Anolis sagrei) in Florida. Using a Bayesian model selection framework, we evaluated whether individual host differences (i.e., body size and sex) or spatial variability (i.e., habitat type and local‐scale host spatial structure) drive heterogeneity in the probability of infection or its associated health costs (i.e., body condition, blood chemistry). We found that the probability of infection increases with increasing lizard body size in both systems. However, in Florida, we found the relationship to be subdued in deforested habitats compared to the adjacent urban hydric forests. Furthermore, infection was spatially clustered within sampling sites, with “hot” and “cold” spots across the landscape. Nevertheless, we found no clear evidence of costs of infection on lizard health in any of the measures assessed and hence no grounds for inference regarding heterogeneous virulence. Ultimately, the consistency of our results across systems suggests prominent roles of individual and spatial heterogeneities as driving factors of transmission of vector‐borne diseases.

Location of Hungarian microbreweries: an exploratory analysis

ABSTRACT Growth in the craft beer sector has been unevenly distributed over time and in terms of geographical area. While the spatial drivers of craft breweries in the United States are well documented, research on the situation in European countries is still limited. This paper investigates the locational determinants of the Hungarian craft brewing industry between 2005 and 2018 at the level of small regions. We employ negative binominal models to determine the relationship between the number of microbreweries and locational factors. We find that demand-side factors, including income, level of education, proportion of youth in a region and population density, positively affect the number of craft breweries. We emphasize that general local economic characteristics may be less important, whilst industry-specific local conditions (e.g., craft brewing traditions and market saturation) significantly impact the location choice of craft breweries. Our estimations highlight the role of transport costs in location decisions. Finally, we demonstrate the relevance of the availability of water, one of most important inputs in beer production.

Fish functional diversity is modulated by small-scale habitat complexity in a temperate ecosystem

Rocky reefs and kelp forests form conspicuous habitats that promote species diversity and support livelihoods in coastal communities. However, taxonomic approaches often disregard biological identity and differences between species. In this study, we explore the relationship between functional diversity of fish communities and habitat complexity in temperate reefs and test if greater habitat complexity, such as rugosity or kelp three-dimensional structure, would result in higher functional diversity. We conducted fish surveys using SCUBA in four kelp forest sites and rocky reef sites. Although the rocky reef and kelp forest sites showed differences in habitat complexity, no significant differences in fish taxonomic or functional diversity were found between the two habitat types. However, we did find differences at smaller spatial scales for the kelp forest sites, where number of stipes, stipe bundle diameter, and kelp density influenced functional richness, but not species richness, highlighting the importance of functional approaches in certain ecosystems. The differences found among kelp forest sites may be linked with small-scale spatiotemporal oceanographic drivers of productivity such as upwelling exposure or nutrient availability. We recommend considering small-scale spatial drivers when aiming to understand how habitat characteristics link with functional diversity.

Analysis of the heterogeneity of landscape risk evolution and driving factors based on a combined GeoDa and Geodetector model

Liaoning Province, one of China’s top 13 grain-producing areas, has recently implemented various ecological construction and protection plans. To ensure regional environmental protection, national food security, and the development of scientific ecological protection strategies, it is crucial to understand the spatial and temporal evolutionary patterns and driving forces of the landscape’s ecological risk. This study used spatial statistics and spatial autocorrelation to examine the scale response features of landscape risk in Liaoning Province. The best study scale was then selected and the GeoDa software platform was used to examine the pattern of landscape risk evolution in Liaoning Province from 2000 to 2020. Finally, the main spatial drivers of ecological risk in Liaoning Province were analyzed hierarchically according to the magnitude of the explanatory power of the q-statistic by combining the driving force-pressure-state-impact-response model with Geodetector. Several experiments were conducted to determine the optimal taxonomy and classification number for the discretization of each driver. The results showed that (1) the Liaoning River had a high-risk value and thus required careful consideration to maintain sustainable and healthy growth of the area and conserve ecological environment of the southeastern coastline of Liaoning Province. (2) The township scale was regarded as the ideal scale because all nine scales of landscape risk indices in Liaoning Province showed positive spatial connections and the greatest significant difference with it. (3) Clustering and significance analysis determined that townships in Liaoning Province with higher landscape risk indices were more likely to cluster spatially and had less spatial variance. (4) The two factors with the greatest impact on landscape risk in Liaoning Province were temperature and gross domestic product per capita. Landscape risk was the result of the combined influence of these multiple factors. Hence, the carrying capacity of the environment itself cannot be ignored while pursuing economic growth. Our findings offer theoretical support for government to formulate ecological protection policies.

Co-occurrences enhance our understanding of aquatic fungal metacommunity assembly and reveal potential host-parasite interactions.

Our knowledge of aquatic fungal communities, their assembly, distributions and ecological roles in marine ecosystems is scarce. Hence, we aimed to investigate fungal metacommunities of coastal habitats in a subarctic zone (northern Baltic Sea, Sweden). Using a novel joint species distribution model and network approach, we quantified the importance of biotic associations contributing to the assembly of mycoplankton, further, detected potential biotic interactions between fungi-algae pairs, respectively. Our long-read metabarcoding approach identified 493 fungal taxa, of which a dominant fraction (44.4%) was assigned as early-diverging fungi (i.e. Cryptomycota and Chytridiomycota). Alpha diversity of mycoplankton declined and community compositions changed along inlet-bay-offshore transects. The distributions of most fungi were rather influenced by environmental factors than by spatial drivers, and the influence of biotic associations was pronounced when environmental filtering was weak. We found great number of co-occurrences (120) among the dominant fungal groups, and the 25 associations between fungal and algal OTUs suggested potential host-parasite and/or saprotroph links, supporting a Cryptomycota-based mycoloop pathway. We emphasize that the contribution of biotic associations to mycoplankton assembly are important to consider in future studies as it helps to improve predictions of species distributions in aquatic ecosystems.

Assessing the concept of control points for dissolved reactive phosphorus losses in subsurface drainage.

Agricultural phosphorus (P) loss, which is highly variable in space and time, has been studied using the hot spot/hot moment concept, but increasing the rigor of these assessments through a relatively newer "ecosystem control point" framework may help better target management practices that provide a disproportionate water quality benefit. Sixteen relatively large (0.85ha) subsurface drainage plots in Illinois were used as individual observational units to assess dissolved reactive P (DRP) concentrations and losses within a given field over four study years. Three plot-months were identified as DRP control points (one export and two transport control points), where each plot-month contributed>10% of the annual DRP load from the field. These control points occurred on separate plots and in both the growing and nongrowing seasons but were likely related to agronomic P applications. Elevated soil test P, especially near a historic farmstead, and soil clay content were spatial drivers of P loss across the field. The nongrowing season was hypothesized to be the most significant period of P loss, but this was only documented in two of the four study years. A cereal rye (Secale cereale L.) cover crop did not significantly reduce DRP loss in any year, but there was also no evidence of increased drainage P losses due to freezing and thawing of the cover crop biomass. This work confirmed annual subsurface drainage DRP losses were agronomically small(<3% of P application rate), although the range of DRP concentrations relative to eutrophication criteria still demonstrated a potential for negative environmental impact. The control point concept may provide a new lens to view drainage DRP losses, but this framework should be refined through additional within-field studies because mechanisms of P export at this field were more nuanced than just the presence of tile drainage (i.e., a transport control point).

Spatial drivers of wetland bird occupancy within an urbanized matrix in the Upper Midwestern United States

AbstractWetland birds are undergoing severe population declines in North America, with habitat degradation and wetland loss considered two of the primary causes. Due to the cryptic nature of many wetland bird species, the ecological conditions (e.g., matrix composition) that influence bird occupancy, and the relevant spatial scales at which to measure bird responses, remain unclear but may affect inference about wetland use and suitability. We conducted wetland bird surveys at 477 points across northeastern Illinois and northwestern Indiana within the highly urbanized landscape surrounding Chicago. Using remotely sensed land cover data, we built occupancy models for 10 wetland bird species (American Coot Fulica americana, Black‐crowned Night‐Heron Nycticorax nycticorax, Blue‐winged Teal Anas discors, Common Gallinule Gallinula galeata, Least Bittern Ixobrychus exilis, Marsh Wren Cistothorus palustris, Pied‐billed Grebe Podilymbus podiceps, Sora Porzana carolina, Swamp Sparrow Melospiza georgiana, and Virginia Rail Rallus limicola) to quantify their responses to wetland cover types (emergent wetland, forested wetland, riverine wetland, and freshwater pond) and urbanization at four spatial scales (200‐, 400‐, 800‐, and 2000‐m radial distances). We also included the distance to Lake Michigan as a covariate in occupancy models to account for ecological differences between coastal and inland wetlands. We found that relationships between land cover types and occupancy differed by species, as did the spatial scale of support. Generally, the presence of emergent wetlands or ponds at immediate (200 m) and local (400 m) spatial scales within the surrounding matrix was positively associated with wetland bird occupancy. Contrary to expectations, we did not find support for a negative relationship between urbanization and occupancy for most focal species, indicating that birds are using available wetland habitats despite surrounding development. While future research should evaluate management strategies at the watershed scale, our findings suggest that wetland conservation planning at immediate and local scales is likely to promote bird habitat use within highly modified landscapes of the Upper Midwestern United States.

Spatial Patterns and Composition Traits of Soil Microbial Nitrogen-Metabolism Genes in the Robinia pseudoacacia Forests at a Regional Scale.

Microbial-driven processes related to the nitrogen-metabolism (N-metabolism) in soil are critical for ecosystem functioning and stability. There are spatial patterns of microbial-mediated nitrogen processes, but we still lack an overview of the soil N-metabolism genes of single nitrogen-fixing tree species pure forests at a regional scale. Here, we investigated the spatial variation and drivers of microbial N-metabolism genes in the rhizosphere soil of Robinia pseudoacacia on the Loess Plateau by metagenomic technology. We found that the distance-decay of soil N functional gene similarities in Robinia pseudoacacia forests on the Loess Plateau spanning a geographic distance of 230 km was significant (p < 0.001). The gene composition and co-occurrence patterns in the process of soil microbial N-metabolism were very different, and they were mainly driven by soil pH and MAP (mean annual precipitation). The proportion of positive links and edges co-occurrence networks between N functional genes increased with increasing pH, suggesting that increasing pH promoted connections between functional genes. The relative frequencies of N-metabolism pathways were consistent on the Loess Plateau, the abundance of ammonia assimilation pathway was highest, and the abundance of the nitrogen fixation pathway was the lowest; only the abundance of the nitrogen fixation pathway was not significantly different. The bacterial and archaeal communities involved in soil nitrogen metabolism were significantly different. Structural equation modeling showed that decreases in soil pH and MAP mainly affected the increase in nitrogen functional gene abundance through an increase in the diversity of N-metabolism microorganisms. In conclusion, this study provides a baseline for biogeographic studies of soil microbe functional genes.

Global Flash Drought Analysis: Uncertainties From Indicators and Datasets

AbstractFlash Drought (FD) has garnered much attention in recent years, with significant advancements in the indicators applied for identifying these rapidly intensifying events. However, the difference in existing FD definitions and methodologies among research communities and the choice of different data sources underscores the importance of addressing the uncertainties associated with the global FD characteristics and their drivers. This study compares two key FD indicators derived based on evaporative stress ratio (ESR) and root‐zone soil‐moisture (RZSM) using three different data sources to investigate the uncertainties in global FD frequency and intensity (speed), and the influencing drivers. The results suggest that such disparities are significant in the two FD indicators across different climate regions of the globe. The results highlight varying spatial drivers of FD frequency, intensity, and their evolution, potentially linked to background aridity. Changes in precipitation, temperature, vapor pressure deficit, and soil‐temperature coupling play an important role with a cascading (concurrent) impact on the evolution of FD based on RZSM (ESR). The relationship between ESR and RZSM fails to explain most of the variance in each of these indicators specific to the FD episodes, especially in the transitional and humid climate regimes. Overall, the results highlight the necessity of more nuanced methodologies for deriving FD indicators that can efficiently couple the rapid soil‐moisture depletion rates in deeper layers with changes in atmospheric evaporative demand which has direct implications on vegetation health.

Optimising restoration and rehabilitation using environmental and spatial drivers of plant assemblages

The extent, condition and connectedness of ecosystems has been significantly impacted by human activities globally, leading to a widespread appeal of and demand for ecological restoration. Maximising the ecological outcomes and cost effectiveness of restoration requires that plans be optimised by indexing the distribution, abundance and condition of habitat-forming species, and that target ecosystem conditions be developed in concert with restoration site attributes. This type of quantitative approach for creating malleable ecological targets which are informed by local environmental conditions is, however, uncommon. In this study, we surveyed the composition and coverage of understorey plants, and the composition and size of trees in coastal dunes at five transects at each of 20 sites (for n = 100) on the Sunshine Coast, central eastern Australia, and use this information to prioritise restoration sites and create optimised planting regimes for degraded sites. Each of the identified indicator species (six understorey and four tree species) had unique preferred conditions and was affected by multiple environmental and spatial variables at varying spatial scales, with most species affected by urbanisation. Species distribution models (SDMs) were used to identify target/reference ecosystems and optimal planting mixes for potential restoration sites given the site’s environmental attributes. Our approach of integrating data on the distribution, abundance and condition of habitat-forming species into multiple SDMs can be used to optimise planting regimes at restoration sites and provides a framework for setting dynamic restoration targets across landscapes.