Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially. To test these hypotheses, we (i) determined which of the environmental variables reported to be the most influential affected WT and (ii)identified the spatial scales over which those environmental variables influenced WT. To this end, the influence of multi-scale environmental variables, namely land cover, topography (channel slope, elevation), hydromorphology (channel sinuosity, water level, watershed area, baseflow index) and shade conditions, was analyzed on the three model variables (day thermal sensitivity, night thermal sensitivity, and non-convective thermal flux) in the model developed by Georges et al. (2021) of the temporal thermal dynamics of daily maximum WT during extreme events. Values were calculated on six spatial scales (the entire upstream catchment and the associated 1 km and 2 km circular buffer, and 50 m wide corridors on each side of the stream with the associated 1 km and 2 km circular buffer). The period considered was 17 extreme days during the summer identified by Georges et al. (2021) based on WT data measured every 10 min for 7 years (2012–2018) at 92 measurement sites. Sites were located evenly throughout the Wallonia (southern Belgium) hydrological network. Results showed that shade, baseflow index (a proxy of the influence of groundwater), water level and watershed area were the most significant variables influencing thermal sensitivity. Since managers with finite financial and human resources can act on only a few environmental variables, we advocate restoring and preserving the vegetation cover that limits solar radiation on the watercourse as a cost-effective solution to reduce thermal sensitivity. Moreover, management at small spatial scale (50 m riparian buffer) should be strategically promoted (for finance and staffing) as our results show that a larger management scale is not more effective in reducing thermal sensitivity to extreme events.

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Multi-scale data on intertidal macrobenthic biodiversity and environmental features in three New Zealand harbours

10.5194/essd-2019-151 ◽

2019 ◽

Author(s):

Casper Kraan ◽

Barry L. Greenfield ◽

Simon F. Thrush

Keyword(s):

New Zealand ◽

Environmental Variables ◽

Spatial Organization ◽

Spatial Scales ◽

Species Distributions ◽

Sediment Grain Size ◽

High Resolution Data ◽

Multi Scale ◽

Grain Size Distributions ◽

Patterns And Processes

Abstract. Understanding how the plants and animals that live in the seafloor vary in their spatial patterns of diversity and abundance is fundamental to gaining insight in the role of biodiversity in maintaining ecosystem functioning in coastal ecosystems, as well as advancing the modelling of species distributions under realistic assumptions. Yet, it is virtually unknown how the relationships between abundance patterns and different biotic and environmental processes change depending on spatial scales, which is mainly due to a lack of data. Within the project Spatial Organization of Species Distributions: Hierarchical and Scale-Dependent Patterns and Processes in Coastal Seascapes at the National Institute for Water and Atmospheric Research (NIWA) in New Zealand we collected multi-scale and high-resolution data on macrobenthic biodiversity. We found 146 species, i.e. bivalves, polychaetes and crustaceans (> 500 μm) that live hidden in marine sandflats, and collected point measurements of important environmental variables (sediment grain-size distributions, chlorophyll a concentration, and visible sandflat parameters) in three large intertidal Harbours (Kaipara, Tauranga and Manukau). In each Harbour we sampled 400 points for macrobenthic community composition and abundances, as well as the full set of environmental variables. Using an elaborate sampling design, we were able to cover scales from 30 centimetres to a maximal extent of 1 km. All data and extensive metadata are available from the data publisher PANGAEA via the persistent identifier https://doi.org/10.1594/PANGAEA.903448.

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Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

Biogeosciences ◽

10.5194/bg-12-3993-2015 ◽

2015 ◽

Vol 12 (13) ◽

pp. 3993-4004 ◽

Cited By ~ 18

Author(s):

U. Mishra ◽

W. J. Riley

Keyword(s):

Environmental Factors ◽

Organic Carbon ◽

Spatial Structure ◽

Spatial Heterogeneity ◽

Spatial Scale ◽

Environmental Variables ◽

Spatial Scales ◽

Statistical Properties ◽

Environmental Controls ◽

Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing the heterogeneity of terrestrial hydrological and biogeochemical processes in Earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a data set with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, and 500 m and 1, 2, 5, and 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 m to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98 % of variability in the variance of SOC stocks. We found moderately accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ∼ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks could improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

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Scaling impacts on environmental controls and spatial heterogeneity of soil organic carbon stocks

Biogeosciences Discussions ◽

10.5194/bgd-12-1721-2015 ◽

2015 ◽

Vol 12 (2) ◽

pp. 1721-1751 ◽

Cited By ~ 2

Author(s):

U. Mishra ◽

W. J. Riley

Keyword(s):

Environmental Factors ◽

Organic Carbon ◽

Spatial Structure ◽

Spatial Heterogeneity ◽

Spatial Scale ◽

Environmental Variables ◽

Spatial Scales ◽

Statistical Properties ◽

Environmental Controls ◽

Soc Stocks

Abstract. The spatial heterogeneity of land surfaces affects energy, moisture, and greenhouse gas exchanges with the atmosphere. However, representing heterogeneity of terrestrial hydrological and biogeochemical processes in earth system models (ESMs) remains a critical scientific challenge. We report the impact of spatial scaling on environmental controls, spatial structure, and statistical properties of soil organic carbon (SOC) stocks across the US state of Alaska. We used soil profile observations and environmental factors such as topography, climate, land cover types, and surficial geology to predict the SOC stocks at a 50 m spatial scale. These spatially heterogeneous estimates provide a dataset with reasonable fidelity to the observations at a sufficiently high resolution to examine the environmental controls on the spatial structure of SOC stocks. We upscaled both the predicted SOC stocks and environmental variables from finer to coarser spatial scales (s = 100, 200, 500 m, 1, 2, 5, 10 km) and generated various statistical properties of SOC stock estimates. We found different environmental factors to be statistically significant predictors at different spatial scales. Only elevation, temperature, potential evapotranspiration, and scrub land cover types were significant predictors at all scales. The strengths of control (the median value of geographically weighted regression coefficients) of these four environmental variables on SOC stocks decreased with increasing scale and were accurately represented using mathematical functions (R2 = 0.83–0.97). The spatial structure of SOC stocks across Alaska changed with spatial scale. Although the variance (sill) and unstructured variability (nugget) of the calculated variograms of SOC stocks decreased exponentially with scale, the correlation length (range) remained relatively constant across scale. The variance of predicted SOC stocks decreased with spatial scale over the range of 50 to ~ 500 m, and remained constant beyond this scale. The fitted exponential function accounted for 98% of variability in the variance of SOC stocks. We found moderately-accurate linear relationships between mean and higher-order moments of predicted SOC stocks (R2 ~ 0.55–0.63). Current ESMs operate at coarse spatial scales (50–100 km), and are therefore unable to represent environmental controllers and spatial heterogeneity of high-latitude SOC stocks consistent with observations. We conclude that improved understanding of the scaling behavior of environmental controls and statistical properties of SOC stocks can improve ESM land model benchmarking and perhaps allow representation of spatial heterogeneity of biogeochemistry at scales finer than those currently resolved by ESMs.

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Optimization of spatial scale, but not functional shape, affects the performance of habitat suitability models: a case study of tigers (Panthera tigris) in Thailand

Landscape Ecology ◽

10.1007/s10980-020-01105-6 ◽

2021 ◽

Vol 36 (2) ◽

pp. 455-474

Author(s):

Eric Ash ◽

David W. Macdonald ◽

Samuel A. Cushman ◽

Adisorn Noochdumrong ◽

Tim Redford ◽

...

Keyword(s):

Habitat Selection ◽

Habitat Suitability ◽

Environmental Variables ◽

Spatial Scales ◽

Model Performance ◽

Panthera Tigris ◽

Habitat Suitability Models ◽

Multi Scale ◽

Single Scale ◽

Optimized Model

Abstract Context Species habitat suitability models rarely incorporate multiple spatial scales or functional shapes of a species’ response to covariates. Optimizing models for these factors may produce more robust, reliable, and informative habitat suitability models, which can be beneficial for the conservation of rare and endangered species, such as tigers (Panthera tigris). Objectives We provide the first formal assessment of the relative impacts of scale-optimization and shape-optimization on model performance and habitat suitability predictions. We explored how optimization influences conclusions regarding habitat selection and mapped probability of occurrence. Methods We collated environmental variables expected to affect tiger occurrence, calculating focal statistics and landscape metrics at spatial scales ranging from 250 m to 16 km. We then constructed a set of presence–absence generalized linear models including: (1) single-scale optimized models (SSO); (2) a multi-scale optimized model (MSO); (3) single-scale shape-optimized models (SSSO) and (4) a multi-scale- and shape-optimized model (MSSO). We compared performance and resulting prediction maps for top performing models. Results The SSO (16 km), SSSO (16 km), MSO, and MSSO models performed equally well (AUC > 0.9). However, these differed substantially in prediction and mapped habitat suitability, leading to different ecological understanding and potentially divergent conservation recommendations. Habitat selection was highly scale-dependent and the strongest relationships with environmental variables were at the broadest scales analysed. Modelling approach had a substantial influence in variable importance among top models. Conclusions Our results suggest that optimization of the scale of resource selection is crucial in modelling tiger habitat selection. However, in this analysis, shape-optimization did not improve model performance.

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Multi-scale data on intertidal macrobenthic biodiversity and environmental features in three New Zealand harbours

Earth System Science Data ◽

10.5194/essd-12-293-2020 ◽

2020 ◽

Vol 12 (1) ◽

pp. 293-297

Author(s):

Casper Kraan ◽

Barry L. Greenfield ◽

Simon F. Thrush

Keyword(s):

New Zealand ◽

Environmental Variables ◽

Spatial Organization ◽

Spatial Scales ◽

Species Distributions ◽

Organic Content ◽

Sediment Grain Size ◽

High Resolution Data ◽

Multi Scale ◽

Patterns And Processes

Abstract. Understanding how the plants and animals that live in the sea floor vary in their spatial patterns of diversity and abundance is fundamental to gaining insight into the role of biodiversity in maintaining ecosystem functioning in coastal ecosystems, as well as advancing the modelling of species distributions under realistic assumptions. Yet, it is virtually unknown how the relationships between abundance patterns and different biotic and environmental processes change depending on spatial scales, which is mainly due to a lack of data. Within the project Spatial Organization of Species Distributions: Hierarchical and Scale-Dependent Patterns and Processes in Coastal Seascapes at the National Institute for Water and Atmospheric Research (NIWA) in New Zealand we collected multi-scale and high-resolution data on macrobenthic biodiversity. We found 146 species dominated by bivalves, polychaetes, and crustaceans (>500 µm) that live hidden in marine sandflats and collected point measurements of important environmental variables (sediment grain-size distributions, chlorophyll a concentration, organic content, and visible sandflat parameters) in three large intertidal harbours (Kaipara, Tauranga, and Manukau). In each harbour we sampled 400 points for macrobenthic community composition and abundances, as well as the full set of environmental variables. Using an elaborate sampling design, we were able to cover scales from 30 cm to a maximal extent of 1 km. All data and extensive metadata are available from the data publisher PANGAEA via the persistent identifier https://doi.org/10.1594/PANGAEA.903448 (Kraan et al., 2019).

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Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis

Geospatial health ◽

10.4081/gh.2021.1024 ◽

2021 ◽

Vol 16 (2) ◽

Author(s):

Renfei Yang ◽

Fu Ren ◽

Xiangyuan Ma ◽

Hongwei Zhang ◽

Wenxuan Xu ◽

...

Keyword(s):

Gender Differences ◽

Environmental Factors ◽

Environmental Effects ◽

Geographically Weighted Regression ◽

Spatial Scales ◽

Social Activity ◽

Weighted Regression ◽

Multi Scale ◽

And Gender ◽

Longevity Index

Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.

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Genomic variation in the American pika: signatures of geographic isolation and implications for conservation

BMC Ecology and Evolution ◽

10.1186/s12862-020-01739-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kelly B. Klingler ◽

Joshua P. Jahner ◽

Thomas L. Parchman ◽

Chris Ray ◽

Mary M. Peacock

Keyword(s):

Genetic Variation ◽

Genetic Structure ◽

Sierra Nevada ◽

Spatial Genetic Structure ◽

Spatial Scales ◽

Thermal Sensitivity ◽

Genomic Variation ◽

Genome Wide ◽

A Genome ◽

American Pika

Abstract Background Distributional responses by alpine taxa to repeated, glacial-interglacial cycles throughout the last two million years have significantly influenced the spatial genetic structure of populations. These effects have been exacerbated for the American pika (Ochotona princeps), a small alpine lagomorph constrained by thermal sensitivity and a limited dispersal capacity. As a species of conservation concern, long-term lack of gene flow has important consequences for landscape genetic structure and levels of diversity within populations. Here, we use reduced representation sequencing (ddRADseq) to provide a genome-wide perspective on patterns of genetic variation across pika populations representing distinct subspecies. To investigate how landscape and environmental features shape genetic variation, we collected genetic samples from distinct geographic regions as well as across finer spatial scales in two geographically proximate mountain ranges of eastern Nevada. Results Our genome-wide analyses corroborate range-wide, mitochondrial subspecific designations and reveal pronounced fine-scale population structure between the Ruby Mountains and East Humboldt Range of eastern Nevada. Populations in Nevada were characterized by low genetic diversity (π = 0.0006–0.0009; θW = 0.0005–0.0007) relative to populations in California (π = 0.0014–0.0019; θW = 0.0011–0.0017) and the Rocky Mountains (π = 0.0025–0.0027; θW = 0.0021–0.0024), indicating substantial genetic drift in these isolated populations. Tajima’s D was positive for all sites (D = 0.240–0.811), consistent with recent contraction in population sizes range-wide. Conclusions Substantial influences of geography, elevation and climate variables on genetic differentiation were also detected and may interact with the regional effects of anthropogenic climate change to force the loss of unique genetic lineages through continued population extirpations in the Great Basin and Sierra Nevada.

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The Winter Habitat Selection of Red Deer (Cervus elaphus) Based on a Multi-Scale Model

Animals ◽

10.3390/ani10122454 ◽

2020 ◽

Vol 10 (12) ◽

pp. 2454

Author(s):

Yue Sun ◽

Yanze Yu ◽

Jinhao Guo ◽

Minghai Zhang

Keyword(s):

Habitat Selection ◽

Environmental Factors ◽

Cervus Elaphus ◽

Red Deer ◽

Scale Model ◽

Winter Habitat ◽

Multi Scale ◽

Optimal Scale ◽

Scale Models ◽

Selection Of

Single-scale frameworks are often used to analyze the habitat selections of species. Research on habitat selection can be significantly improved using multi-scale models that enable greater in-depth analyses of the scale dependence between species and specific environmental factors. In this study, the winter habitat selection of red deer in the Gogostaihanwula Nature Reserve, Inner Mongolia, was studied using a multi-scale model. Each selected covariate was included in multi-scale models at their “characteristic scale”, and we used an all subsets approach and model selection framework to assess habitat selection. The results showed that: (1) Univariate logistic regression analysis showed that the response scale of red deer to environmental factors was different among different covariate. The optimal scale of the single covariate was 800–3200 m, slope (SLP), altitude (ELE), and ratio of deciduous broad-leaved forests were 800 m in large scale, except that the farmland ratio was 200 m in fine scale. The optimal scale of road density and grassland ratio is both 1600 m, and the optimal scale of net forest production capacity is 3200 m; (2) distance to forest edges, distance to cement roads, distance to villages, altitude, distance to all road, and slope of the region were the most important factors affecting winter habitat selection. The outcomes of this study indicate that future studies on the effectiveness of habitat selections will benefit from multi-scale models. In addition to increasing interpretive and predictive capabilities, multi-scale habitat selection models enhance our understanding of how species respond to their environments and contribute to the formulation of effective conservation and management strategies for ungulata.

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Complex community responses underpin biodiversity change following invasion

Biological Invasions ◽

10.1007/s10530-021-02559-8 ◽

2021 ◽

Author(s):

Alessandra R. Kortz ◽

Anne E. Magurran

Keyword(s):

Invasive Species ◽

Species Richness ◽

Spatial Scales ◽

Species Abundance ◽

Ecological Systems ◽

Multi Scale ◽

Mechanistic Approach ◽

Α Diversity ◽

Biodiversity Change ◽

Insight Into

AbstractHow do invasive species change native biodiversity? One reason why this long-standing question remains challenging to answer could be because the main focus of the invasion literature has been on shifts in species richness (a measure of α-diversity). As the underlying components of community structure—intraspecific aggregation, interspecific density and the species abundance distribution (SAD)—are potentially impacted in different ways during invasion, trends in species richness provide only limited insight into the mechanisms leading to biodiversity change. In addition, these impacts can be manifested in distinct ways at different spatial scales. Here we take advantage of the new Measurement of Biodiversity (MoB) framework to reanalyse data collected in an invasion front in the Brazilian Cerrado biodiversity hotspot. We show that, by using the MoB multi-scale approach, we are able to link reductions in species richness in invaded sites to restructuring in the SAD. This restructuring takes the form of lower evenness in sites invaded by pines relative to sites without pines. Shifts in aggregation also occur. There is a clear signature of spatial scale in biodiversity change linked to the presence of an invasive species. These results demonstrate how the MoB approach can play an important role in helping invasion ecologists, field biologists and conservation managers move towards a more mechanistic approach to detecting and interpreting changes in ecological systems following invasion.

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The influence of multi-scale environmental variables on the distribution of terricolous lichens in a fog desert

Journal of Vegetation Science ◽

10.1111/j.1654-1103.2006.tb02506.x ◽

2006 ◽

Vol 17 (6) ◽

pp. 831-838 ◽

Cited By ~ 15

Author(s):

Jennifer S. Lalley ◽

Heather A. Viles ◽

Neil Copeman ◽

Catherine Cowley

Keyword(s):

Environmental Variables ◽

Multi Scale ◽

Terricolous Lichens

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