Spatial Grain Research Articles

A global database of butterfly species native distributions.

Butterflies represent a diverse group of insects, playing key ecosystem roles such as pollination and their larval form engage in herbivory. Despite their importance, comprehensive global distribution data for butterfly species are lacking. This lack of comprehensive global data has hindered many large-scale questions in ecology, evolutionary biology, and conservation at the regional and global scales. Here, I use an integrative workflow that combines occurrence records, alpha hull polygons, species' dispersal capacity, and natural habitat and environmental variables within a framework of species distribution models to generate species-level native distributions for butterflies at a global scale in the contemporary period. The database releases native range maps for 10,372 extant species of butterflies at a spatial grain resolution of 5 arcmin (~10 km). This database has the potential to allow unprecedented large-scale analyses in ecology, biogeography, and conservation of butterflies. The maps are available in the WGS84 coordinate reference system (EPSG:4326 code) and stored as vector polygons in the GEOPACKAGE format for maximum compression, allowing easy data manipulation using a standard computer. I additionally provide each species' spatial raster. All maps and R scripts are open access and available for download in Dryad and Zenodo, respectively, and are guided by FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. By making these data available to the scientific community, I aim to advance the sharing of biological data to stimulate more comprehensive research in ecology, biogeography, and conservation of butterflies.

Unravelling spatial scale effects on elevational diversity gradients: insights from montane small mammals in Kenya

BackgroundMontane ecosystems play crucial roles as global biodiversity hotspots. However, climatic changes and anthropogenic pressure increasingly threaten the stability of montane community dynamics, such as diversity-elevation interactions, creating a challenge in understanding species biogeography and community ecology dynamics in these crucial conservation areas. We examined how varying sampling spatial grains influence small mammal diversity patterns within Kenya’s tallest montane ecosystems.MethodsEmploying a combination of multidimensional alpha diversity metrics and multisite beta diversity characteristics (species richness, phylogenetic and functional diversity and divergence, and multisite beta diversity) alongside spatial generalized additive multivariate regression analyses, we tested how spatial scaling influences elevational diversity gradient patterns and their associations with environmental and human activity variables.ResultsThe diversity-elevation associations were generally homogeneous across spatial grains; however, idiosyncratic patterns emerged across mountains. The total (taxonomic, phylogenetic, and functional) beta diversity, nestedness, and turnover resultant components monotonically increased or decreased with varying spatial grains. The associations between the diversity patterns and the environmental and human footprint variables increased with spatial grain size but also presented variations across mountains and indices. Species richness and phylogenetic and functional richness indices were more strongly influenced by spatial scale variations than were the divergence and community structure indices in both the diversity distribution patterns and their associations with the environmental and human variables.ConclusionsThe diversity-elevation and diversity-environment (including human activity pressure) relationships across spatial grains suggest that montane small mammal diversity patterns portray subtle but systematic sensitivity to sampling spatial grain variation and underscore the importance of geographical context in shaping these elevational diversity gradients. For improved effectiveness, conservation efforts should consider these spatial effects and the unique geographical background of individual montane ecosystems.

Effect of environmental DNA sampling resolution in detecting nearshore fish biodiversity compared to capture surveys.

Sampling and sequencing marine environmental DNA (eDNA) provides a tool that can increase our ability to monitor biodiversity, but movement and mixing of eDNA after release from organisms before collection could affect our inference of species distributions. To assess how conditions at differing spatial scales influence the inferred species richness and compositional turnover, we conducted a paired eDNA metabarcoding and capture (beach seining) survey of fishes on the coast of British Columbia. We found more taxa were typically detected using eDNA compared to beach seining. eDNA identified more taxa with alternative habitat preferences, and this richness difference was greater in areas of high seawater movement, suggesting eDNA has a larger spatial grain influenced by water motion. By contrast, we found that eDNA consistently missed low biomass species present in seining surveys. Spatial turnover of communities surveyed using beach seining differed from that of the eDNA and was better explained by factors that vary at small (10-1000s meters) spatial scales. Specifically, vegetation cover and shoreline exposure explained most species turnover from seining, while eDNA turnover was not explained by those factors and showed a distance decay pattern (a change from 10% to 25% similarity from 2 km to 10 km of distance), suggesting unmeasured environmental variation at larger scales drives its turnover. Our findings indicate that the eDNA sample grain is larger than that of capture surveys. Whereas seining can detect differences in fish distributions at scales of 10s-100s of meters, eDNA can best summarize fish biodiversity at larger scales possibly more relevant to regional biodiversity assessments.

Rapid evaluation of habitat connectivity change to safeguard multispecies persistence in human-transformed landscapes

Following the adoption of the Post-2020 Kunming-Montreal Global Biodiversity Framework (KM-GBF) of the Convention on Biological Diversity (CBD) there is a clear science-policy need to protect habitat connectivity and track its change over time to safeguard biodiversity and inform conservation planning. In response to this need we describe an analytical, multi-indicator and multispecies approach for the rapid assessment of habitat connectivity at fine spatial grain and at the extent of an entire ecoregion. Out of 68 connectivity indicators we found through a literature review, we identified a key-set of six indicators that align with the Essential Biodiversity Variables framework and are suitable to guide rapid action for connectivity and conservation targets in the KM-GBF. Using these selected indicators, we mapped and evaluated connectivity change from 2011 to 2021 across the ecoregion of the St-Lawrence Lowlands in Quebec (~ 30,000 km2) for seven ecoprofile species representing regional forest habitat needs. For most of these species, trends over the last decade indicate a decline in effective connected area and metapopulation carrying capacity, via a division of large contiguous habitat into smaller fragments, whereas on average, habitat area slightly increased. These results highlight that temporal changes in habitat area and connectivity are not necessarily correlated and the urgent need to conserve and restore connectivity to meet targets under the KM-GBF. We provide an R-tool to support our general approach, which enables a comprehensive evaluation of connectivity for regional spatial planning for biodiversity in regions with moderate to high human disturbance.

Predicting plants in the wild: Mapping arctic and boreal plants with UAS-based visible and near infrared reflectance spectra

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data (<10 cm pixels) is often necessary to resolve patches of many Arctic and boreal plant groups, such as bryophytes and lichens, which are significant components of terrestrial vegetation cover. Separation of co-occurring small vegetation patches in images also requires high spectral resolution. Our goal here was to test the capabilities of UAS-based imaging spectroscopy for mapping plant functional types (PFT) using high spatial and spectral resolution data over Arctic and boreal vegetation at four sites in central Alaska. We then tested several Machine and Deep learning models of PFT cover using the reflectance spectra. The best models were very simple, balancing both bias (overfitting caused by imbalance sample sizes) and variance (fit to the independent validation data), explaining > 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

Peak beta diversity occurs at regional spatial grains

Beta diversity (β) quantifies the dissimilarity between communities and is thus fundamental to biogeography and spatial ecology. However, multiple and conflicting hypotheses exist to explain how β varies with spatial scale (here grain within a constant extent). Some resolution on spatial scaling of β may help clarify long-standing debate about: (a) β itself and (b) the ecological community concept, because the clearest spatial size of an ecological community corresponds to the scale with maximal β. Here we test five alternative hypotheses for β spatial scaling by calculating β for four very different taxa (ants, birds, diatoms, and trees) at spatial grains ranging from one to one million km2 in a constant spatial extent (contiguous USA). We also test repeatability for: (a) summer and winter bird β in six consecutive years; (b) trees through time (4 years, spaced 5 years apart). Specifically, we organize point data into hexagonal polygons, and then calculate mean pairwise β between each hexagon and its neighbors. We also calculate the deviation of β from two null models (assuming either spatial heterogeneity or homogeneity). For multiple reasons, β deviation based on spatial heterogeneity was preferable to that based on spatial homogeneity. For all taxa, β peaks at regional scales: trees ~300 km2; winter birds ~500 km2; summer birds ~2000 km2; ants ~2000 km2; diatoms ~11,000 km2. Also, spatial scaling is consistent inter-annually (birds, trees) and sensitive to seasonality (birds). Results broadly support the regional community concept and, based on the variety of taxa evaluated here, similar patterns are likely across the tree of life. Local ecological and evolutionary forces scale up to form regional community patterns and results here support efforts to coordinate local conservation efforts in regions (e.g., corridors and networks) to best conserve biodiversity. Beta diversity peaked at substantial spatial scales for all taxa evaluated here (ants, birds, diatoms, trees), suggesting a general pattern. The regional community concept advanced by Robert Ricklefs is supported empirically, in preference to four other hypotheses also tested. Beta diversity calculations used a relatively new deviation from a spatially heterogeneous null, which modestly adjusted observed beta diversity. Results support use of this newer beta deviation rather than the older beta deviation based on spatial homogeneity. Given that ecological communities appear regional in scale, local biodiversity conservation work can best ensure success if it is regionally coordinated.

MEDIS—A comprehensive spatial database on Mediterranean islands for biogeographical and evolutionary research

AbstractMotivationThe intrinsic characteristics of islands make them a unique study system for the investigation of ecological and evolutionary dynamics. The Mediterranean Basin, an island‐rich biodiversity hotspot, still lacks a comprehensive spatial database for these geographic features. This study presents the first comprehensive spatial database of all Mediterranean islands larger than 0.01 km2, aiding ecological investigations and interdisciplinary research.Main types of variable containedThe MEDIS spatial database offers detailed information on 39 geographic, climatic, ecological and land‐use variables, including island area, perimeter, isolation metrics, climatic space, terrain data, land cover, palaeogeography, road networks and geological information, providing a multifaceted view of each island's characteristics.Spatial location and grainThe study encompasses 2217 islands in the Mediterranean Basin larger than 0.01 km2. The spatial grain of the datasets on which the selected variables are based varies from 10 m (ESA WorldCover) to 1 km (CHELSA‐BIOCLIM+).Time period and grainThe spatial database incorporates data from various sources, each with its own timeframe, such as the Global Shoreline Vector from 2014 Landsat imagery and the WorldCover dataset from 2021. Historical data like the Paleocoastlines GIS dataset offer insights into island configurations during the Last Glacial Maximum.Major taxa and level of measurementWhile not focusing on specific taxa, the study lays the foundation for comprehensive research on Mediterranean islands, facilitating comparisons and investigations into the distribution of native, endemic or alien species. The level of measurement is extensive, encompassing a wide range of variables and providing polygonal features rather than centroids' coordinates.

Spatio-Temporal Changes and Driving Forces Analysis of Urban Open Spaces in Shanghai between 1980 and 2020: An Integrated Geospatial Approach

Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based on long-sequence time series. Based on the Google Earth Engine (GEE) platform and using the Random Forest (RF) classifier, multiple techniques, namely landscape metrics, trend analysis, open space ratio, transition matrix, Normalized Difference Vegetation Index (NDVI), and fractal dimension analysis, were applied to analyze the Landsat satellite data. Next, Geographic Detector (GeoDetector) methods were used to investigate the driving forces of such spatial variations. The results showed that (1) the RF classification algorithm, supported by the GEE, can accurately and quickly obtain a research object dataset, and that calculating the optimal spatial grain size for open space pattern was 70 m; (2) open spaces exhibited declining and contracting trends; and open spaces in the city experienced a decline from 91.83% in 1980 to 69.63% in 2020. Meanwhile, the degree of open spaces in each district increased to different extents, whilst connectivity markedly decreased. Furthermore, the open space of city center districts showed the lowest rate of decrease, with open space patterns fragmenting due to encroaching urbanization; (3) the contribution of socioeconomic factors to the spatial–temporal changes in open space continually has increased over the past 40 years, and were also higher than natural geographic factors to some extent. Apart from offering policy insights guiding the future spatial planning and development of the city, this paper has contributions from both methodological and empirical perspectives. Based on integrated remote sensing and geographic information science (GIS) techniques, this paper provides updated evidence and a clearer understanding of the spatiotemporal variations in urban spaces and their influencing mechanisms in Shanghai.

Dealing with area‐to‐point spatial misalignment in species distribution models

Species distribution models (SDMs) are extensively used to estimate species–environment relationships (SERs) and predict species distribution across space and time. For this purpose, it is key to choose relevant spatial grains for predictor and response variables at the onset of the modelling process. However, environmental variables are often derived from large‐scale climate models at a grain that can be coarser than the one of the response variable. Such area‐to‐point spatial misalignment can bias estimates of SER and jeopardise the robustness of predictions. We used a virtual species approach, running simulations across different levels of area‐to‐point spatial misalignment to seek statistical solutions to this problem. We specifically compared accuracy of SER estimates and predictive performances, assessed across different degrees of spatial heterogeneity in environmental conditions, of three SDMs: a GLM, a spatial GLM and a Berkson error model (BEM) that accounts for fine‐grain environmental heterogeneity within coarse‐grain cells. Only the BEM accurately estimates SER from relatively coarse‐grain environmental data (up to 50 times coarser than the response grain), while the two GLMs provide flattened SER. However, all three models perform poorly when predicting from coarse‐grain data, particularly in environments that are more heterogeneous than the training conditions. Conversely, decreasing environmental heterogeneity relative to the training dataset reduces the predictive biases. Because predictions are made from covariate‐grain data, the BEM displays lower predictive performance than the two GLMs. Thus, standard model selection methods would fail to select the model that best estimates SERs (here, the BEM), which could lead to false interpretations about the environmental drivers of species distributions. Overall, we conclude that the BEM, because it can robustly estimate SER at the response grain, holds great promise to overcome area‐to‐point misalignment.

REVISITING GLOBAL ENDEMISM PATTERNS OF TERRESTRIAL VERTEBRATES AND THEIR ENVIRONMENTAL PREDICTORS: PUTTING THE FOCUS ON SPECIES RANGE-RESTRICTIONS AT FINE SPATIAL GRAINS

Global endemism assessments at fine spatial grains can offer many information about the endemic species richness that inhabit an area, but unable to provide insights on to what extent species distribution ranges are restricted to that area. This inability to account for species range-restrictions prevents recognizing the level of species uniqueness to particular sites, which is necessary to both understand the processes involved in the maintenance of endemism patterns and effectively conserve worldwide biodiversity. To circumvent this problem, the research proposes to use an index capable of measuring the proportion of species restricted to small spatial units. Using species range maps of terrestrial vertebrates, it was assessed global endemism patterns based on species range-restrictions as a counter position to the common use of species richness. Furthermore, it has been evaluated the role that different environmental variables have as predictors of these patterns through simultaneous autoregressive regression. Results showed the highest reported congruence among vertebrate endemism patterns, which suggest the influence of similar processes that affect species range-restrictions through space. Environmental variables explained more than 68% of the total variance on vertebrate endemism, so it is discussed their role as factors that promote species range-restrictions by limiting organism geographic distributions.

Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening.

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVIGS) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVIGS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVIGS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

Impact of temporal beach grain size variability on aeolian sediment transport and topographic evolution in a microtidal environment

While the impact of the spatio/temporal variability of grain size on morphological beach state is reasonably well understood, relatively little is known on its impact at a scale of days/months on aeolian sediment transport. This study focuses on five short intensive wind events during which aeolian sediment transport measurements, beach surface sampling and elevation change surveys were carried out for 1 to 3 days, over a 16-month period on a microtidal beach dominated by offshore winds. Monthly observations show a high temporal variability in beach grain size in relation to the decoupling between hydrodynamic and aeolian processes, from medium sand after a marine storm and inundation of the beach, to very coarse sand after several weeks of storm-force winds. During each wind event, topographic change on the beach ranged from zero, to 0.55 m. The time scale of coarsening depended on the initial beach grain size and could be very fast when the beach was composed of medium sand (e.g. 388 μm sand changed to coarse sand of 547 μm in 40 h). In contrast, it took one month to transition from a coarse median beach grain size of 883 μm to a very coarse one of 1323 μm. This variability in grain size results in dramatically different rates of sediment flux. For example, during average wind speeds of 10 to 14 m/s the sediment flux when the beach was composed of medium sized sand ranged between 21 and 154 kg/m/h compared to 0.4 to 50 kg/m/h when the beach was composed of coarse and very coarse sized grains. Overall, this study highlights the importance of beach grain size variability on aeolian sediment transport and shows that for similar incident wind and climatic conditions aeolian sediment transport rates vary dramatically. The study demonstrates the importance of taking care when using a constant median grain size in the calculation of long-term aeolian sand transport on beaches with heterogeneous spatial and temporal beach grain size variability.

The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot.

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Putting space into trait ecology: Trait, environment and biodiversity relationships at multiple spatial scales

Abstract Ecological processes such as environmental filtering and biotic interactions that shape species' traits and community diversity often vary with geographic distance, potentially generating spatial structures in trait variation, covariation and biodiversity data. Understanding spatial structures of trait, environment and biodiversity, or the spatial link between those factors, is fundamental to identifying spatially explicit assembly processes or biodiversity distributions in spatially heterogeneous landscapes but remains unclear. To address the issue, we gathered individual‐level leaf and diameter traits data paired with environmental data from a 4.8 ha subtropical Chinese forest and divided the forest into 25, 100, 400 and 1936 m2 grids representing contrasting spatial grains. Using Moran's correlograms, we quantified the spatial structures of trait variation and covariation, environmental conditions and biodiversity. We assessed the links between those variables using path analyses. Most variables were spatially positively autocorrelated. However, trait mean was more autocorrelated than trait variation or covariation, and intraspecific trait variation was more autocorrelated than interspecific variation. Autocorrelations in those community properties were generally weak at the large grain. Path analyses indicated positive associations between interspecific trait variation and species diversity at a very small to medium scale and a positive association between intraspecific variation and small‐scale functional diversity. Trait covariation constrained biodiversity, and multi‐trait means were negatively linked to very small‐ to medium‐scale species diversity but positively to medium‐ and large‐scale functional diversity. Patterns regarding multi‐trait community structure–environment–biodiversity associations were generally held for individual traits. However, depending on the trait, spatial scale and plant ontogenic stage, the pattern's strength changed, or occasionally, their sign reversed. We attribute spatial patterns in multi‐trait mean and covariation to scale‐dependent variation in environmental heterogeneity and trait variation to scale‐dependent competition. Synthesis. Our study provides novel insights into spatial and scale‐dependent variability in functional community structure, environment and biodiversity, and their relationships. Our results demonstrate the usefulness of spatial trait analyses in identifying scale‐dependent assembly processes or finding the importance of processes to biodiversity distributions in spatially heterogeneous landscapes. A spatially explicit perspective is thus helpful for the progress of trait ecology.

Disentangling fine‐ and large‐scale colonization processes in metapopulation dynamics: a case study on a threatened epiphytic bryophyte

Understanding the colonization process of species living in a dynamic fragmented habitat is essential to assess their persistence. In the metapopulation theory, the colonization of a species can be quantified using the turnover of occupancy in habitat patches. However, this approach is often limited by the feasible size of surveyed areas. Because many species are capable of long‐distance dispersal, such areas often constitute open systems undergoing colonization of propagules coming from outside, the ‘background deposition'. We focus on disentangling background deposition from local colonization among surveyed patches when analyzing turnover. We consider two spatial scales: 1) focal areas where all patches are monitored over time; 2) a larger extent, encompassing the focal areas, over which the distribution of the target species is quantified with a coarse spatial grain. Our key idea is to use the regional connectivity of focal areas within the larger scale as a covariate when analyzing colonization events within focal areas. A positive effect of regional connectivity on the colonization probability of patches may indicate background deposition. We applied this approach to the epiphytic bryophyte Dicranum viride in a managed temperate deciduous forest, considering phorophyte trees as patches, forest stands as focal areas and the whole forest as the larger scale. We combined a fine‐grained turnover survey of occupied trees within three forest stands (~ 3 ha) with a coarse‐grained snapshot of D. viride distribution over the forest (~ 15 000 ha). Regional connectivity came out as the most significant factor, with a strong positive effect on colonization probability within stands. However, it was attributed to sources in the immediate vicinity of focal stands, suggesting a short‐ranged colonization process occurring across stands' borders rather than long‐distance background deposition. Our results thus call for maintaining a stepping‐stone of habitat across the forest through time to improve D. viride persistence.

Ultra-fine resolution of pre-saccadic attention in the fovea

Microsaccades, the tiny gaze relocations that occurr during fixation, have been linked to covert attention deployed degrees away from the center of gaze. However, the link between attention and microsaccades is deeper in that it also unfolds at the foveal scale. Here, we have examined the spatial grain of pre-microsaccadic attention across the 1° foveola. Through the use of high-precision eye-tracking and gaze-contingent display system that achieves arcminute precision in gaze localization, we have shown that the spotlight of attention at this scale can reach a strikingly high resolution, in the order of 0.17°. Further, when a microsaccade occurs, vision is modulated in a peculiar way across the foveola; whereas fine spatial vision is enhanced at the microsaccade goal location, it drops at the very center of gaze, where acuity is normally highest. Theseresults reveal the finesse of the visuomotor system and of the interplay between eye movements and attention.

The effectiveness of species distribution models in predicting local abundance depends on model grain size.

The use of species distribution models (SDMs) to predict local abundance has been often proposed and contested. We tested whether SDMs at different spatiotemporal resolutions may predict the local density of 14 bird species of open/semi-open habitats. SDMs were built at 1 ha and 1 km, and with long-term versus a mix of current and long-term climatic variables. The estimated environmental suitability was used to predict local abundance obtained by means of 275 linear transects. We tested SDM ability to predict abundance for all sampled sites versus occurrence sites, using N-mixture models to account for imperfect detection. Then, we related the R2 of N-mixture models to SDM traits. Fine-grain SDMs appeared generally more robust than large-grain ones. Considering the all-transects models, for all species environmental suitability displayed a positive and highly significant effect at all the four combinations of spatial and temporal grains. When focusing only on occurrence transects, at the 1 km grain only one species showed a significant and positive effect. At the 1 ha grain, 62% of species models showed (over both climatic sets) a significant or nearly significant positive effect of environmental suitability on abundance. Grain was the only factor significantly affecting the model's explanatory power: 1 km grain led to lower amounts of variation explained by models. Our work re-opens the debate about predicting abundance using SDM-derived suitability, emphasizing the importance of grains and of spatiotemporal resolution more in general. The incorporation of local variables into SDMs at fine grains is key to predict local abundance. SDMs worked out at really fine grains, approaching the average size of territory or home range of target species, are needed to predict local abundance effectively. This may result from the fact that each single cell may represent a potential territory/home range, and hence a higher suitability over a given area means that more potential territories occur there.

Decomposing biodiversity change to processes of extinction, colonization, and recurrence across scales

Temporal biodiversity change involves colonization, extinction, and recurrence of species. These processes vary with spatial grain (i.e. the area at which biodiversity is assessed), but there is little theory to explain this. Here, we present theoretical scenarios showing that colonization, extinction, and recurrence of species can either increase or decrease in strength across grain size. We tested for these patterns in empirical data on Czech birds over 30 years, and several orders of magnitude of spatial grain. We found that colonization increased from local to national scales, while extinctions followed a hump‐shaped pattern, leading to a higher temporal increase of richness towards coarse grains. Probabilities of colonization and extinction decreased with grain size, with a steeper decrease for extinction. Our results hold independently across two time periods (1985–2002 and 2002–2017), and colonization is the dominant process behind temporal change of richness. This decomposition of biodiversity change allowed us to identify scale‐wise ecological mechanisms driving biodiversity change, and explain seemingly confusing directions of biodiversity change at different spatial scales.

Machine learning assessment of hydrological model performance under localized water storage changes through downscaling

The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) data has limited its application in the management of local-scale water resources. To address this limitation, we developed a new downscaling approach using predictors from regional and global hydrological models for a 15-year period (2002–2017) and tested it in the northern Great Artesian Basin, Australia. We used four different machine learning algorithms (support vector machine, partial least squares, gaussian process and random forest) to downscale the original GRACE estimate of 0.5° to a spatial grain size of 0.1° (global) and 0.05° (regional). This was based on precipitation, evapotranspiration and runoff estimates from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Australian Water Outlook (AWO) hydrological models, respectively. The downscaled products were validated using 42 in-situ precipitation observations spread across the test region. We further evaluated which of the downscaled products best mimicked local-scale hydrology using a range of statistical metrics. Our results showed that regional hydrological models best characterized the dynamics of local scale hydrology (rainfall v. downscaled product), and the gaussian process regression algorithm made the best predictions for both models. The correlation coefficients for the raw values varied from 0.45 to 0.49 while that of the standardized values varied from 0.46 to 0.52 with the random forest model providing the best fitting for the regional-based products. The regional downscaling approach employed in this study may be readily integrated into local water resources planning programs.

Development of a cellular automata-based distributed hydrological model for simulating urban surface runoff

The present study developed a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM) to simulate two-dimensional urban surface runoff processes. CA-DUSRM uses an improved nonlinear reservoir algorithm to simulate the generation of surface runoff outflow at grid cell scale and flow interactions among cells. The CA-DUSRM simulation results (total surface runoff and peak discharge) for an asphalt road sub-catchment during different rainfall events were in good agreement with actual measurements of both surface runoff and rainfall processes. The performance of CA-DUSRM was comprehensively and systematically analyzed under different rainfall–terrain scenarios. The results showed that increasing the temporal grain resulted in uncertain simulation results in relation to the rainfall intensity, slope, and terrain undulation employed; and significant spatial grain effects were mainly caused by large terrain undulation changes. Compared with the storm water management model (SWMM), CA-DUSRM performed better overall and was more sensitive to variation in terrain undulation and fluctuations in rainfall. Based on model structure and simulation mechanism perspectives, CA-DUSRM can present the influence of spatially heterogeneous underlying surface characteristics on the runoff processes and describe the lateral water exchanges, which makes the simulated processes closer to the actual processes when compared with SWMM. CA-DUSRM is potentially suitable for application in urban areas that have complex underlying surfaces.