scholarly journals Spatial Scale Dependence of Ecological Factors That Regulate Functional and Phylogenetic Assembly in a Mediterranean High Mountain Grassland

2021 ◽  
Vol 9 ◽  
Author(s):  
David S. Pescador ◽  
Francesco de Bello ◽  
Jesús López-Angulo ◽  
Fernando Valladares ◽  
Adrián Escudero
Keyword(s):  
Global Warming ◽  
Organic Carbon ◽  
Spatial Scale ◽  
Community Assembly ◽  
Species Interactions ◽  
Species Turnover ◽  
Spatial Scales ◽  
Ecological Factors ◽  
Functional Heterogeneity ◽  
Multi Scale

Understanding how functional and phylogenetic patterns vary among scales and along ecological gradients within a given species pool is critical for inferring community assembly processes. However, we lack a clear understanding of these patterns in stressful habitats such as Mediterranean high mountains where ongoing global warming is expected to affect species fitness and species interactions, and subsequently species turnover. In this study, we investigated 39 grasslands with the same type of plant community and very little species turnover across an elevation gradient above the treeline at Sierra de Guadarrama National Park in central Spain. In particular, we assessed functional and phylogenetic patterns, including functional heterogeneity, using a multi-scale approach (cells, subplots, and plots) and determined the relevance of key ecological factors (i.e., elevation, potential solar radiation, pH, soil organic carbon, species richness, and functional heterogeneity) that affect functional and phylogenetic patterns at each spatial scale. Overall, at the plot scale, coexisting species tended to be more functionally and phylogenetically similar. By contrast, at the subplot and cell scales, species tended to be more functionally different but phylogenetically similar. Functional heterogeneity at the cell scale was comparable to the variation across plots along the gradient. The relevance of ecological factors that regulate diversity patterns varied among spatial scales. An increase in elevation resulted in functional clustering at larger scales and phylogenetic overdispersion at a smaller scale. The soil pH and organic carbon levels exhibited complex functional patterns, especially at small spatial scales, where an increase in pH led to clustering patterns for the traits related to the leaf economic spectrum (i.e., foliar thickness, specific leaf area, and leaf dry matter content). Our findings confirm the presence of primary environmental filters (coldness and summer drought at our study sites) that constrain the regional species pool, suggesting the presence of additional assembly mechanisms that act at the smallest scale (e.g., micro-environmental gradients and/or species interactions). Functional and phylogenetic relatedness should be determined using a multi-scale approach to help interpret community assembly processes and understand the initial community responses to environmental changes, including ongoing global warming.

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

2015 ◽  
Vol 12 (13) ◽  
pp. 3993-4004 ◽  
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.

scholarly journals A dynamic occupancy model for interacting species with two spatial scales

2020 ◽  
Author(s):  
Eivind Flittie Kleiven ◽  
Frederic Barraquand ◽  
Olivier Gimenez ◽  
John-André Henden ◽  
Rolf Anker Ims ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Species Interactions ◽  
Spatial Scales ◽  
Species Interaction ◽  
Camera Traps ◽  
Occupancy Models ◽  
Occupancy Model ◽  
Interacting Species ◽  
Multi Scale ◽  
Extinction Probabilities

1AbstractOccupancy models have been developed independently to account for multiple spatial scales and species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant in models of interacting species. Here we bridge these two model frameworks by developing a multi-scale two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities - including probabilities conditional to the other species’ presence. With a simulation study, we demonstrate that the model is able to estimate parameters without bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities. We further show the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator-prey system. The field study illustrates that the model allows estimation of species interaction effects on colonization and extinction probabilities at two spatial scales. This creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasted movement ranges with camera traps.

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

2015 ◽  
Vol 12 (2) ◽  
pp. 1721-1751 ◽  
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.

scholarly journals Quantification of Myocyte Disarray in Human Cardiac Tissue

2021 ◽  
Vol 12 ◽  
Author(s):  
Francesco Giardini ◽  
Erica Lazzeri ◽  
Giulia Vitale ◽  
Cecilia Ferrantini ◽  
Irene Costantini ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Cardiac Tissue ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Structural Data ◽  
Multi Scale ◽  
Tissue Organization ◽  
Human Cardiac Tissue ◽  
Microscopy Techniques ◽  
Scale Of Analysis

Proper three-dimensional (3D)-cardiomyocyte orientation is important for an effective tension production in cardiac muscle. Cardiac diseases can cause severe remodeling processes in the heart, such as cellular misalignment, that can affect both the electrical and mechanical functions of the organ. To date, a proven methodology to map and quantify myocytes disarray in massive samples is missing. In this study, we present an experimental pipeline to reconstruct and analyze the 3D cardiomyocyte architecture in massive samples. We employed tissue clearing, staining, and advanced microscopy techniques to detect sarcomeres in relatively large human myocardial strips with micrometric resolution. Z-bands periodicity was exploited in a frequency analysis approach to extract the 3D myofilament orientation, providing an orientation map used to characterize the tissue organization at different spatial scales. As a proof-of-principle, we applied the proposed method to healthy and pathologically remodeled human cardiac tissue strips. Preliminary results suggest the reliability of the method: strips from a healthy donor are characterized by a well-organized tissue, where the local disarray is log-normally distributed and slightly depends on the spatial scale of analysis; on the contrary, pathological strips show pronounced tissue disorganization, characterized by local disarray significantly dependent on the spatial scale of analysis. A virtual sample generator is developed to link this multi-scale disarray analysis with the underlying cellular architecture. This approach allowed us to quantitatively assess tissue organization in terms of 3D myocyte angular dispersion and may pave the way for developing novel predictive models based on structural data at cellular resolution.

scholarly journals Testing the Eltonian Noise Hypothesis in a Species-rich Community

2021 ◽  
Author(s):  
Robert Francis ◽  
David Berg ◽  
Neil Ford ◽  
Ashley Walters
Keyword(s):  
Spatial Scale ◽  
Species Interactions ◽  
Spatial Scales ◽  
Freshwater Mussel ◽  
Strong Interactions ◽  
Ecological Niche Models ◽  
Distribution Models ◽  
Multiple Spatial Scales ◽  
Presence Data ◽  
Negative Relationships

Few ecological niche models (ENM) incorporate the Eltonian niche or examine a population’s niche at multiple spatial scales. We used Bayesian Joint Species Distribution Models (JSDMs) across multiple spatial scales to examine whether freshwater mussel communities in east Texas adhered to the Eltonian Noise Hypothesis, which asserts that species interactions exert greater influence on conspecific species at finer spatial scales. For both abundance and presence data, we observed a statistically greater number of strong interactions at the finest spatial scale compared to larger spatial scales. While 34% of abundance interactions and 24% of presence interactions showed strong positive relationships, only 6% of abundance interactions and 0% of presence interactions showed strong negative relationships. We found that freshwater mussel communities conform to the Eltonian Noise Hypothesis. Inclusion of the Eltonian niche and consideration of spatial scale are necessary to accurately model niches and increase efficacy of ENMs as conservation tools.

scholarly journals Rethinking Biological Invasions as a Metacommunity Problem

2021 ◽  
Vol 8 ◽  
Author(s):  
Bryan L. Brown ◽  
Jacob N. Barney
Keyword(s):  
Biological Invasions ◽  
Community Assembly ◽  
Species Interactions ◽  
Native Species ◽  
Biotic Resistance ◽  
Propagule Pressure ◽  
Regional Scale ◽  
Invasion Biology ◽  
Enemy Release ◽  
Multi Scale

Perhaps more than any other ecological discipline, invasion biology has married the practices of basic science and the application of that science. The conceptual frameworks of population regulation, metapopulations, supply-side ecology, and community assembly have all to some degree informed the regulation, management, and prevention of biological invasions. Invasion biology needs to continue to adopt emerging frameworks and paradigms to progress as both a basic and applied science. This need is urgent as the biological invasion problem continues to worsen. The development of metacommunity theory in the last two decades represents a paradigm-shifting approach to community ecology that emphasizes the multi-scale nature of community assembly and biodiversity regulation. Work on metacommunities has demonstrated that even relatively simple processes at local scales are often heavily influenced by regional-scale processes driven primarily by the dispersal of organisms. Often the influence of dispersal interacts with, or even swamps, the influence of local-scale drivers like environmental conditions and species interactions. An emphasis on dispersal and a focus on multi-scale processes enable metacommunity theory to contribute strongly to the advancement of invasion biology. Propagule pressure of invaders has been identified as one of the most important drivers facilitating invasion, so the metacommunity concept, designed to address how dispersal-driven dynamics affect community structure, can directly address many of the central questions of invasion biology. Here we revisit many of the important concepts and paradigms of biological invasions—propagule pressure, biotic resistance, enemy release, functional traits, neonative species, human-assisted transport,—and view those concepts through the lens of metacommunity theory. In doing so, we accomplish several goals. First, we show that work on metacommunities has generated multiple predictions, models, and the tools that can be directly applied to invasion scenarios. Among these predictions is that invasibility of a community should decrease with both local controls on community assembly, and the dispersal rates of native species. Second, we demonstrate that framing biological invasions in metacommunity terms actually unifies several seemingly disparate concepts central to invasion biology. Finally, we recommend several courses of action for the control and management of invasive species that emerge from applying the concepts of metacommunity theory.

scholarly journals Iterative guided image fusion

2016 ◽  
Vol 2 ◽  
pp. e80 ◽  
Author(s):  
Alexander Toet
Keyword(s):  
Image Fusion ◽  
Spatial Scale ◽  
Spatial Scales ◽  
Small Scale ◽  
Computationally Efficient ◽  
Multi Scale ◽  
Saliency Maps ◽  
Spatial Consistency ◽  
Guided Filtering ◽  
Fusion Scheme

We propose a multi-scale image fusion scheme based on guided filtering. Guided filtering can effectively reduce noise while preserving detail boundaries. When applied in an iterative mode, guided filtering selectively eliminates small scale details while restoring larger scale edges. The proposed multi-scale image fusion scheme achieves spatial consistency by using guided filtering both at the decomposition and at the recombination stage of the multi-scale fusion process. First, size-selective iterative guided filtering is applied to decompose the source images into approximation and residual layers at multiple spatial scales. Then, frequency-tuned filtering is used to compute saliency maps at successive spatial scales. Next, at each spatial scale binary weighting maps are obtained as the pixelwise maximum of corresponding source saliency maps. Guided filtering of the binary weighting maps with their corresponding source images as guidance images serves to reduce noise and to restore spatial consistency. The final fused image is obtained as the weighted recombination of the individual residual layers and the mean of the approximation layers at the coarsest spatial scale. Application to multiband visual (intensified) and thermal infrared imagery demonstrates that the proposed method obtains state-of-the-art performance for the fusion of multispectral nightvision images. The method has a simple implementation and is computationally efficient.

scholarly journals Tropical land-use change alters trait-based community assembly rules for dung beetles and birds

Oecologia ◽  
2021 ◽  
Author(s):  
Felicity A. Edwards ◽  
David P. Edwards ◽  
Keith C. Hamer ◽  
Tom M. Fayle
Keyword(s):  
Community Assembly ◽  
Species Interactions ◽  
Spatial Scales ◽  
Dung Beetles ◽  
Habitat Associations ◽  
Habitat Modification ◽  
Forest Community ◽  
Primary Forest ◽  
Disturbance Gradient ◽  
Habitat Generalists

AbstractTropical rainforest disturbance and conversion are critical drivers of biodiversity loss. A key knowledge gap is understanding the impacts of habitat modification on mechanisms of community assembly, which are predicted to respond differently between taxa and across spatial scales. We use a null model approach to detect trait assembly of species at local- and landscape-scales, and then subdivide communities with different habitat associations and foraging guilds to investigate whether the detection of assembly mechanisms varies between groups. We focus on two indicator taxa, dung beetles and birds, across a disturbance gradient of primary rainforest, selectively logged rainforest, and oil palm plantations in Borneo, Southeast Asia. Random community assembly was predominant for dung beetles across habitats, whereas trait convergence, indicative of environmental filtering, occurred across the disturbance gradient for birds. Assembly patterns at the two spatial scales were similar. Subdividing for habitat association and foraging guild revealed patterns hidden when focusing on the overall community. Dung beetle forest specialists and habitat generalists showed opposing assembly mechanisms in primary forest, community assembly of habitat generalists for both taxa differed with disturbance intensity, and insectivorous birds strongly influenced overall community assembly relative to other guilds. Our study reveals the sensitivity of community assembly mechanisms to anthropogenic disturbance via a shift in the relative contribution of stochastic and deterministic processes. This highlights the need for greater understanding of how habitat modification alters species interactions and the importance of incorporating species’ traits within assessments.

scholarly journals A general framework for quantitatively assessing ecological stochasticity

2019 ◽  
Vol 116 (34) ◽  
pp. 16892-16898 ◽  
Author(s):  
Daliang Ning ◽  
Ye Deng ◽  
James M. Tiedje ◽  
Jizhong Zhou
Keyword(s):  
Organic Carbon ◽  
Vegetable Oil ◽  
Community Assembly ◽  
Spatial Scales ◽  
Null Model ◽  
Environmental Noise ◽  
Mathematical Framework ◽  
Oil Injection ◽  
Very High ◽  
Null Expectation

Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio (NST), was developed with 50% as the boundary point between more deterministic (<50%) and more stochastic (>50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic (NST = 21%) to more stochastic (NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic (NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.

scholarly journals Multi-scale Cover Selection by White-tailed Deer, Odocoileus virginianus, in an Agro-forested Landscape

2009 ◽  
Vol 123 (1) ◽  
pp. 32
Author(s):  
Tim L. Hiller ◽  
Henry Campa ◽  
Scott R. Winterstein
Keyword(s):  
Spatial Scale ◽  
Odocoileus Virginianus ◽  
Resource Selection ◽  
Selection Process ◽  
Spatial Scales ◽  
Relative Importance ◽  
Multi Scale ◽  
South Central ◽  
Hierarchical Selection ◽  
And Behavior

Resource selection studies are commonly conducted at a single spatial scale, but this likely does not fully or accurately assess the hierarchical selection process used by animals. We used a multi-spatial scale approach to quantify White-tailed Deer (Odocoileus virginianus) cover selection in south-central Michigan during 2004–2006 by varying definitions of use and availability and ranking the relative importance of cover types under each study design. The number of cover types assigned as selected (proportional use > proportional availability) decreased from coarse (landscape level) to fine (within home range) scales, although at finer scales, selection seemed to be more consistent. Although the relative importance changed substantially across spatial scales, two cover types (conifers, upland deciduous forests) were consistently ranked as the two most important, providing strong evidence of their value to deer in the study area. Testing for resource selection patterns using a multi-spatial scale approach would provide additional insight into the ecology and behavior of a particular species.

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