scholarly journals Disturbance Effects on Spatial Autocorrelation in Biodiversity: An Overview and a Call for Study

Diversity ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 167
Author(s):  
Shekhar R. Biswas ◽  
Jingyin Xiang ◽  
Hui Li
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Species Interactions ◽  
Spatial Scales ◽  
Ecological Processes ◽  
Natural Systems ◽  
Restoration Planning ◽  
Habitat Disturbances ◽  
Disturbance Effects ◽  
Habitat Scale

The spatially autocorrelated patterns of biodiversity can be an important determinant of ecological processes, functions and delivery of services across spatial scales. Therefore, understanding disturbance effects on spatial autocorrelation in biodiversity is crucial for conservation and restoration planning but remains unclear. In a survey of disturbance versus spatial patterns of biodiversity literature from forests, grasslands and savannah ecosystems, we found that habitat disturbances generally reduce the spatial autocorrelation in species diversity on average by 15.5% and reduce its range (the distance up to which autocorrelation prevails) by 21.4%, in part, due to disturbance-driven changes in environmental conditions, dispersal, species interactions, or a combination of these processes. The observed effect of disturbance, however, varied markedly among the scale of disturbance (patch-scale versus habitat-scale). Surprisingly, few studies have examined disturbance effects on the spatial patterns of functional diversity, and the overall effect was non-significant. Despite major knowledge gaps in certain areas, our analysis offers a much-needed initial insights into the disturbance-driven changes in the spatial patterns of biodiversity, thereby setting the ground for informed discussion on conservation and promotion of spatial heterogeneity in managing natural systems under a changing world.

scholarly journals How sample heterogeneity can obscure the signal of microbial interactions

2019 ◽  
Author(s):  
David W. Armitage ◽  
Stuart E. Jones
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Species Interactions ◽  
Dynamic Models ◽  
Spatial Scales ◽  
Microbial Interactions ◽  
Vital Rates ◽  
Ecological Processes ◽  
Correlation Networks ◽  
Community Data

ABSTRACTMicrobial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. Researchers applying these methods assume that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species’ (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena — Simpson’s paradox, context-dependence, and nonlinear averaging — can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometres) and those of typical microbial community samples (millimetres to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.

scholarly journals Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns

2021 ◽  
Author(s):  
Yuval R. Zelnik ◽  
Matthieu Barbier ◽  
David W. Shanafelt ◽  
Michel Loreau ◽  
Rachel M. Germain
Keyword(s):  
Species Interactions ◽  
Large Scale ◽  
Spatial Scales ◽  
Landscape Patterns ◽  
Ecological Processes ◽  
As Species ◽  
Species Area ◽  
Non Local ◽  
Systematic Understanding ◽  
Pattern Scale

Ecology is a science of scale, which guides our description of both ecological processes and patterns, but we lack a systematic understanding of how process scale and pattern scale are connected. Recent calls for a synthesis between population ecology, community ecology, and ecosystem ecology motivate the integration of phenomena at multiple levels of organization. Furthermore, many studies leave out the scaling of a critical process: species interactions, which may be non-local through mobility or vectors (resources or species) and must be distinguished from dispersal scales. Here, we use simulations to explore the consequences of different process scales (i.e. species interactions, dispersal, and the environment) on emergent patterns of biodiversity, ecosystem functioning, and their relationship, in a spatially-explicit landscape. A major result of our study is that the spatial scales of dispersal and species interactions have opposite effects: a larger dispersal scale homogenizes spatial biomass patterns, while a larger interaction scale amplifies their heterogeneity. We find that an interesting interplay between process scales occurs when the spatial distribution of species is heterogeneous at large scales, i.e., when the environment is not too uniform and dispersal not very strong. Interestingly, the specific scale at which scales of dispersal and interactions begin to influence landscape patterns depends on the environmental heterogeneity of the landscape -- in other words, the scale of one process allows important scales to emerge in other processes. Finally, contrary to our expectations, we observe that the spatial scale of ecological processes is more clearly reflected in landscape patterns (i.e. distribution of local outcomes) than in global patterns such as Species-Area Relationships or large-scale biodiversity-functioning relationships.

scholarly journals Topographic Effects on the Spatial Species Associations in Diverse Heterogeneous Tropical Evergreen Forests

2021 ◽  
Vol 13 (5) ◽  
pp. 2468
Author(s):  
Nguyen Hong Hai ◽  
Yousef Erfanifard ◽  
Van Bac Bui ◽  
Trinh Hien Mai ◽  
Any Mary Petritan ◽  
...  
Keyword(s):  
Species Interactions ◽  
Species Coexistence ◽  
Spatial Scales ◽  
Interspecific Interactions ◽  
Spatial Association ◽  
Topographic Effects ◽  
Habitat Association ◽  
Important Species ◽  
Evergreen Forests ◽  
Partial Overlap

Studying spatial patterns and habitat association of plant communities may provide understanding of the ecological mechanisms and processes that maintain species coexistence. To conduct assessments of correlation between community compositions and habitat association, we used data from two topographically different plots with 2 ha area in tropical evergreen forests with the variables recorded via grid systems of 10 × 10 m subplots in Northern-Central Vietnam. First, we tested the relationship between community composition and species diversity indices considering the topographical variables. We then assessed the interspecific interactions of 20 dominant plant species using the nearest-neighbor distribution function, Dij(r), and Ripley’s K-function, Kij(r). Based on the significant spatial association of species pairs, indices of interspecific interaction were calculated by the quantitative amounts of the summary statistics. The results showed that (i) community compositions were significantly influenced by the topographic variables and (ii) almost 50% significant pairs of species interactions were increased with increasing spatial scales up to 10–15 m, then declined and disappeared at scales of 30–40 m. Segregation and partial overlap were the dominant association types and disappeared at larger spatial scales. Spatial segregation, mixing, and partial overlap revealed the important species interactions in maintaining species coexistence under habitat heterogeneity in diverse forest communities.

scholarly journals Are Distribution Patterns Correlated with Plant Traits?

2021 ◽  
Author(s):  
◽  
Benjamin Magana-Rodriguez
Keyword(s):  
Life History ◽  
Leaf Area ◽  
Spatial Patterns ◽  
Life History Traits ◽  
Multiple Scales ◽  
Spatial Scales ◽  
Regional Scale ◽  
Distribution Patterns ◽  
Spatial Arrangement ◽  
Scaling Relationship

<p>The current crisis in loss of biodiversity requires rapid action. Knowledge of species' distribution patterns across scales is of high importance in determining their current status. However, species display many different distribution patterns on multiple scales. A positive relationship between regional (broad-scale) distribution and local abundance (fine-scale) of species is almost a constant pattern in macroecology. Nevertheless interspecific relationships typically contain much scatter. For example, species that possess high local abundance and narrow ranges, or species that are widespread, but locally rare. One way to describe these spatial features of distribution patterns is by analysing the scaling properties of occupancy (e.g., aggregation) in combination with knowledge of the processes that are generating the specific spatial pattern (e.g., reproduction, dispersal, and colonisation). The main goal of my research was to investigate if distribution patterns correlate with plant life-history traits across multiple scales. First, I compared the performance of five empirical models for their ability to describe the scaling relationship of occupancy in two datasets from Molesworth Station, New Zealand. Secondly, I analysed the association between spatial patterns and life history traits at two spatial scales in an assemblage of 46 grassland species in Molesworth Station. The spatial arrangement was quantified using the parameter k from the Negative Binomial Distribution (NBD). Finally, I investigated the same association between spatial patterns and life-history traits across local, regional and national scales, focusing in one of the most diverse families of plant species in New Zealand, the Veronica sect. Hebe (Plantaginaceae). The spatial arrangement was investigated using the mass fractal dimension. Cross-species correlations and phylogenetically independent contrasts were used to investigate the relationships between plant life-history traits and spatial patterns on both data bases. There was no superior occupancy-area model overall for describing the scaling relationship, however the results showed that a variety of occupancy-area models can be fit to different data sets at diverse spatial scales using nonlinear regression. Additionally, here I showed that it is possible to deduce and extrapolate information on occupancy at fine scales from coarse-scale data. For the 46 plantassemblage in Molesworth Station, Specific leaf area (SLA) exhibits a positive association with aggregation in cross-species analysis, while leaf area showed a negative association, and dispersule mass a positive correlation with degree of aggregation in phylogenetic contrast analysis at a local-scale (20 × 20 m resolution). Plant height was the only life-history trait that was associated with degree of aggregation at a regional-scale (100 × 60 mresolution). For the Veronica sect. Hebe dataset, leaf area showed a positive correlation with aggregation while specific leaf area showed a negative correlation with aggregation at a fine local-scale (2.5-60 m resolution). Inflorescence length, breeding system and leaf area showed a negative correlation with degree of aggregation at a regional-scale (2.5-20 km resolution). Height was positively associated with aggregation at national-scale (20-100 km resolution). Although life-history traits showed low predictive ability in explaining aggregation throughout this thesis, there was a general pattern about which processes and traits were important at different scales. At local scales traits related to dispersal and completion such as SLA , leaf area, dispersule mass and the presence of structures in seeds for dispersal, were important; while at regional scales traits related to reproduction such as breeding system, inflorescence length and traits related to dispersal (seed mass) were significant. At national scales only plant height was important in predicting aggregation. Here, it was illustrated how the parameters of these scaling models capture an important aspect of spatial pattern that can be related to other macroecological relationships and the life-history traits of species. This study shows that when several scales of analysis are considered, we can improve our understanding about the factors that are related to species' distribution patterns.</p>

scholarly journals Local versus site-level effects of algae on coral microbial communities

2021 ◽  
Vol 8 (9) ◽  
pp. 210035
Author(s):  
Amy A. Briggs ◽  
Anya L. Brown ◽  
Craig W. Osenberg
Keyword(s):  
Microbial Communities ◽  
Spatial Scales ◽  
French Polynesia ◽  
Negative Effects ◽  
Ecological Processes ◽  
Antagonistic Effects ◽  
Alpha And Beta Diversity ◽  
Macroalgal Cover ◽  
Coral Microbiome ◽  
Turf Algae

Microbes influence ecological processes, including the dynamics and health of macro-organisms and their interactions with other species. In coral reefs, microbes mediate negative effects of algae on corals when corals are in contact with algae. However, it is unknown whether these effects extend to larger spatial scales, such as at sites with high algal densities. We investigated how local algal contact and site-level macroalgal cover influenced coral microbial communities in a field study at two islands in French Polynesia, Mo'orea and Mangareva. At 5 sites at each island, we sampled prokaryotic microbial communities (microbiomes) associated with corals, macroalgae, turf algae and water, with coral samples taken from individuals that were isolated from or in contact with turf or macroalgae. Algal contact and macroalgal cover had antagonistic effects on coral microbiome alpha and beta diversity. Additionally, coral microbiomes shifted and became more similar to macroalgal microbiomes at sites with high macroalgal cover and with algal contact, although the microbial taxa that changed varied by island. Our results indicate that coral microbiomes can be affected by algae outside of the coral's immediate vicinity, and local- and site-level effects of algae can obscure each other's effects when both scales are not considered.

scholarly journals Identifying the spatial patterns of housing distribution in Johor Bahru through spatial autocorrelation

2020 ◽  
Vol 540 ◽  
pp. 012008
Author(s):  
N A Mohd Sairi ◽  
B Burhan ◽  
E E Mohd Safian
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns

scholarly journals MODELLING LANDSCAPE MORPHODYNAMICS BY TERRESTRIAL PHOTOGRAMMETRY: AN APPLICATION TO BEACH AND FLUVIAL SYSTEMS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1175-1182 ◽  
Author(s):  
E. Sánchez-García ◽  
A. Balaguer-Beser ◽  
R. Taborda ◽  
J. E. Pardo-Pascual
Keyword(s):  
Morphological Changes ◽  
Spatial Scales ◽  
Cost Effective ◽  
Point Clouds ◽  
Field Work ◽  
Fluvial Systems ◽  
Natural Systems ◽  
3D Point Clouds ◽  
Pilot Channel ◽  
Terrestrial Photogrammetry

Beach and fluvial systems are highly dynamic environments, being constantly modified by the action of different natural and anthropic phenomena. To understand their behaviour and to support a sustainable management of these fragile environments, it is very important to have access to cost-effective tools. These methods should be supported on cutting-edge technologies that allow monitoring the dynamics of the natural systems with high periodicity and repeatability at different temporal and spatial scales instead the tedious and expensive field-work that has been carried out up to date. The work herein presented analyses the potential of terrestrial photogrammetry to describe beach morphology. Data processing and generation of high resolution 3D point clouds and derived DEMs is supported by the commercial Agisoft PhotoScan. Model validation is done by comparison of the differences in the elevation among the photogrammetric point cloud and the GPS data along different beach profiles. Results obtained denote the potential that the photogrammetry 3D modelling has to monitor morphological changes and natural events getting differences between 6 and 25 cm. Furthermore, the usefulness of these techniques to control the layout of a fluvial system is tested by the performance of some modeling essays in a hydraulic pilot channel.

Landscape Heterogeneity and Dynamics

2019 ◽  
pp. 42-126
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Spatial Heterogeneity ◽  
Landscape Structure ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Anthropogenic Disturbances ◽  
Ecological Processes ◽  
Disturbance Dynamics ◽  
Wide Range ◽  
Over Time

Heterogeneity is a defining characteristic of landscapes and therefore central to the study of landscape ecology. Landscape ecology investigates what factors give rise to heterogeneity, how that heterogeneity is maintained or altered by natural and anthropogenic disturbances, and how heterogeneity ultimately influences ecological processes and flows across the landscape. Because heterogeneity is expressed across a wide range of spatial scales, the landscape perspective can be applied to address these sorts of questions at any level of ecological organization, and in aquatic and marine systems as well as terrestrial ones. Disturbances—both natural and anthropogenic—are a ubiquitous feature of any landscape, contributing to its structure and dynamics. Although the focus in landscape ecology is typically on spatial heterogeneity, disturbance dynamics produce changes in landscape structure over time as well as in space. Heterogeneity and disturbance dynamics are thus inextricably linked and are therefore covered together in this chapter.

Scaling Issues in Landscape Ecology

2019 ◽  
pp. 14-41
Author(s):  
Kimberly A. With
Keyword(s):  
Landscape Ecology ◽  
Spatial Patterns ◽  
Spatial Scales ◽  
Ecological Data ◽  
Ecological Forecasting ◽  
Spatial And Temporal Scales ◽  
Time Periods ◽  
Patterns And Processes ◽  
Short Time ◽  
Over Time

Spatial patterns are ubiquitous in nature, and ecological systems exhibit patchiness (heterogeneity) across a range of spatial and temporal scales. Landscape ecology is explicitly concerned with understanding how scale affects the measurement of heterogeneity and the scale(s) at which spatial pattern is important for ecological phenomena. Patterns and processes measured at fine spatial scales and over short time periods are unlikely to behave similarly at broader scales and extended time periods. An understanding of pattern-process linkages, a major research focus in landscape ecology, thus requires an understanding of how patterns change with scale, spatially and temporally. The development of methods for extrapolating information across scales is necessary for predicting how landscapes will change over time as well as for ecological forecasting. This chapter explores how scaling issues affect ecological investigations, discusses problems in identifying the correct scale for research, and outlines when and how ecological data can be extrapolated.

scholarly journals Variability in Mixed Conifer Spatial Structure Changes Understory Light Environments

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1015 ◽  
Author(s):  
Jeffery B. Cannon ◽  
Wade T. Tinkham ◽  
Ryan K. DeAngelis ◽  
Edward M. Hill ◽  
Mike A. Battaglia
Keyword(s):  
Spatial Patterns ◽  
Ponderosa Pine ◽  
Spatial Scales ◽  
Light Availability ◽  
Structural Complexity ◽  
Tree Regeneration ◽  
Conifer Forests ◽  
Mixed Conifer ◽  
Regeneration Dynamics ◽  
Wide Range

In fire-adapted conifer forests of the Western U.S., changing land use has led to increased forest densities and fuel conditions partly responsible for increasing the extent of high-severity wildfires in the region. In response, land managers often use mechanical thinning treatments to reduce fuels and increase overstory structural complexity, which can help improve stand resilience and restore complex spatial patterns that once characterized these stands. The outcomes of these treatments can vary greatly, resulting in a large gradient in aggregation of residual overstory trees. However, there is limited information on how a range of spatial outcomes from restoration treatments can influence structural complexity and tree regeneration dynamics in mixed conifer stands. In this study, we model understory light levels across a range of forest density in a stem-mapped dry mixed conifer forest and apply this model to simulated stem maps that are similar in residual basal area yet vary in degree of spatial complexity. We found that light availability was best modeled by residual stand density index and that consideration of forest structure at multiple spatial scales is important for predicting light availability. Second, we found that restoration treatments differing in spatial pattern may differ markedly in their achievement of objectives such as density reduction, maintenance of horizontal and tree size complexity, and creation of microsite conditions favorable to shade-intolerant species, with several notable tradeoffs. These conditions in turn have cascading effects on regeneration dynamics, treatment longevity, fire behavior, and resilience to disturbances. In our study, treatments with high aggregation of residual trees best balanced multiple objectives typically used in ponderosa pine and dry mixed conifer forests. Simulation studies that consider a wide range of possible spatial patterns can complement field studies and provide predictions of the impacts of mechanical treatments on a large range of potential ecological effects.

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