scholarly journals The effect of landscape structure on the evolution of two alternative dispersal strategies

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
C. S. Sieger ◽  
T. Hovestadt
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Local Adaptation ◽  
Landscape Structure ◽  
Nearest Neighbor ◽  
Landscape Heterogeneity ◽  
Gradual Transition ◽  
Environmental Niche ◽  
Spatial Sorting ◽  
Spatio Temporal

Abstract Background Dispersal is an important event for most organisms at least once in their life cycle. The evolution of dispersal can be influenced by local adaptation, landscape structure, and perceived temporal and spatial variation. The interaction between local adaptation, landscape heterogeneity, temporal variability and rules of dispersal may be more complex than previously assumed. Therefore, we sought to understand the influence of emigration rules and landscape structure on emerging dispersal rates and traits. Here, we implemented an individual-based model (IBM) of trait evolution in scenarios characterized by different landscape structures and different degrees of spatial heterogeneity and global temporal variation. Individuals could evolve two traits coding for their environmental niche (position of niche optimum and niche width), and two traits determining nearest-neighbor dispersal: an individual emigrates with a probability defined by the first trait (random emigration), but emigrates with certainty if the fertility expected in the patch of residence falls below a threshold specified by the second trait (habitat-dependent emigration). Results We note an interaction effect between dispersal strategy and spatial variance—lower emigration under habitat-dependent than under random emigration if spatial heterogeneity is low, but eventually a reversal of this ranking if heterogeneity becomes large. Landscapes with sharp transition of habitat attributes result in a high degree of spatial sorting, while fractal landscapes do not. Emigration rates are overall lowest, when spatial variation is highest. Conclusions We conclude that emergent emigration rates are influenced more by landscape structure and spatio-temporal heterogeneity than by the emigration strategy. With the ongoing land use change more research into this topic could help highlight the difficulties species might face under the change from landscapes characterized by gradual transition zones to landscapes dominated by abrupt ecotones, the latter typical for agricultural and urban settings.

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.

scholarly journals Explaining Spatial Heterogeneity in Population Dynamics and Genetics from Spatial Variation in Resources for a Large Herbivore

PLoS ONE ◽  
2012 ◽  
Vol 7 (10) ◽  
pp. e47858 ◽  
Author(s):  
Adrienne L. Contasti ◽  
Emily J. Tissier ◽  
Jill F. Johnstone ◽  
Philip D. McLoughlin
Keyword(s):  
Population Dynamics ◽  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Large Herbivore

scholarly journals Savannah Phenological Dynamics Reveal Spatio-Temporal Landscape Heterogeneity in Karamoja Sub-region, Uganda

2020 ◽  
Vol 4 ◽  
Author(s):  
Anthony Egeru ◽  
John Paul Magaya ◽  
Derick Ansyijar Kuule ◽  
Aggrey Siya ◽  
Anthony Gidudu ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landscape Heterogeneity ◽  
Global Environmental Change ◽  
Arid Environment ◽  
Modis Ndvi ◽  
Moderate Resolution ◽  
Spatio Temporal ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Phenological Metrics

Phenological properties are critical in understanding global environmental change patterns. This study analyzed phenological dynamics in a savannah dominated semi-arid environment of Uganda. We used moderate-resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) imagery. TIMESAT program was used to analyse the imagery to determine key phenological metrics; onset of greenness (OGT), onset of greenness value, end of greenness time (EGT), end of greenness value, maximum NDVI, time of maximum NDVI, duration of greenup (DOG) and range of normalized difference vegetation index (RNDVI). Results showed that thicket and shrubs had the earliest OGT on day 85 ± 14, EGT on day 244 ± 32 and a DOG of 158 ± 25 days. Woodland had the highest NDVI value for maximum NDVI, OGT, EGT, and RNDVI. In the bushland, OGT occurs on average around day 90 ± 11, EGT on day 255 ± 33 with a DOG of 163 ± 36 days. The grassland showed that OGT occurs on day 96 ± 13, EGT on day 252 ± 36 with a total DOG of 156 ± 33 days. Early photosynthesis activity was observed in central to eastern Karamoja in the districts of Moroto and Kotido. There was a positive relationship between rainfall and NDVI across all vegetation cover types as well as between phenological parameters and season dynamics. Vegetation senescence in the sub-region occurs around August to mid-September (day 244–253). The varied phenophases observed in the sub-region reveal an inherent landscape heterogeneity that is beneficial to extensive pastoral livestock production. Continuous monitoring of savannah phenological patterns in the sub-region is required to decipher landscape ecosystem processes and functioning.

scholarly journals Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology

2018 ◽  
Vol 115 (28) ◽  
pp. 7374-7379 ◽  
Author(s):  
Lauren A. White ◽  
James D. Forester ◽  
Meggan E. Craft
Keyword(s):  
Spatial Heterogeneity ◽  
Landscape Structure ◽  
Spatial Organization ◽  
Complex Disease ◽  
Animal Movement ◽  
Pathogen Transmission ◽  
Movement Behavior ◽  
Disease Dynamics ◽  
Conspecific Density ◽  
Pathogen Persistence

Disease models have provided conflicting evidence as to whether spatial heterogeneity promotes or impedes pathogen persistence. Moreover, there has been limited theoretical investigation into how animal movement behavior interacts with the spatial organization of resources (e.g., clustered, random, uniform) across a landscape to affect infectious disease dynamics. Importantly, spatial heterogeneity of resources can sometimes lead to nonlinear or counterintuitive outcomes depending on the host and pathogen system. There is a clear need to develop a general theoretical framework that could be used to create testable predictions for specific host–pathogen systems. Here, we develop an individual-based model integrated with movement ecology approaches to investigate how host movement behaviors interact with landscape heterogeneity (in the form of various levels of resource abundance and clustering) to affect pathogen dynamics. For most of the parameter space, our results support the counterintuitive idea that fragmentation promotes pathogen persistence, but this finding was largely dependent on perceptual range of the host, conspecific density, and recovery rate. For simulations with high conspecific density, slower recovery rates, and larger perceptual ranges, more complex disease dynamics emerged, and the most fragmented landscapes were not necessarily the most conducive to outbreaks or pathogen persistence. These results point to the importance of interactions between landscape structure, individual movement behavior, and pathogen transmission for predicting and understanding disease dynamics.

scholarly journals Improving simulated Amazon forest biomass and productivity by including spatial variation in biophysical parameters

2013 ◽  
Vol 10 (4) ◽  
pp. 2255-2272 ◽  
Author(s):  
A. D. A. Castanho ◽  
M. T. Coe ◽  
M. H. Costa ◽  
Y. Malhi ◽  
D. Galbraith ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Primary Productivity ◽  
Amazon Basin ◽  
Net Primary Productivity ◽  
Above Ground Biomass ◽  
Biophysical Parameters ◽  
Simulation Accuracy ◽  
Forest Biome

Abstract. Dynamic vegetation models forced with spatially homogeneous biophysical parameters are capable of producing average productivity and biomass values for the Amazon basin forest biome that are close to the observed estimates, but these models are unable to reproduce observed spatial variability. Recent observational studies have shown substantial regional spatial variability of above-ground productivity and biomass across the Amazon basin, which is believed to be primarily driven by a combination of soil physical and chemical properties. In this study, spatial heterogeneity of vegetation properties is added to the Integrated Biosphere Simulator (IBIS) land surface model, and the simulated productivity and biomass of the Amazon basin are compared to observations from undisturbed forest. The maximum RuBiCo carboxylation capacity (Vcmax) and the woody biomass residence time (τw) were found to be the most important properties determining the modeled spatial variation of above-ground woody net primary productivity and biomass, respectively. Spatial heterogeneity of these properties may lead to simulated spatial variability of 1.8 times in the woody net primary productivity (NPPw) and 2.8 times in the woody above-ground biomass (AGBw). The coefficient of correlation between the modeled and observed woody productivity improved from 0.10 with homogeneous parameters to 0.73 with spatially heterogeneous parameters, while the coefficient of correlation between the simulated and observed woody above-ground biomass improved from 0.33 to 0.88. The results from our analyses with the IBIS dynamic vegetation model demonstrated that using single values for key ecological parameters in the tropical forest biome severely limits simulation accuracy. Clearer understanding of the biophysical mechanisms that drive the spatial variability of carbon allocation, τw and Vcmax is necessary to achieve further improvements to simulation accuracy.

scholarly journals Comparison of Five Spatio-Temporal Satellite Image Fusion Models over Landscapes with Various Spatial Heterogeneity and Temporal Variation

2019 ◽  
Vol 11 (22) ◽  
pp. 2612 ◽  
Author(s):  
Liu ◽  
Ke ◽  
Yin ◽  
Chen ◽  
Im
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Spatial Heterogeneity ◽  
Image Fusion ◽  
Temporal Variation ◽  
Landscape Heterogeneity ◽  
Local Level ◽  
Satellite Image ◽  
Fusion Model ◽  
Large Area

In recent years, many spatial and temporal satellite image fusion (STIF) methods have been developed to solve the problems of trade-off between spatial and temporal resolution of satellite sensors. This study, for the first time, conducted both scene-level and local-level comparison of five state-of-art STIF methods from four categories over landscapes with various spatial heterogeneity and temporal variation. The five STIF methods include the spatial and temporal adaptive reflectance fusion model (STARFM) and Fit-FC model from the weight function-based category, an unmixing-based data fusion (UBDF) method from the unmixing-based category, the one-pair learning method from the learning-based category, and the Flexible Spatiotemporal DAta Fusion (FSDAF) method from hybrid category. The relationship between the performances of the STIF methods and scene-level and local-level landscape heterogeneity index (LHI) and temporal variation index (TVI) were analyzed. Our results showed that (1) the FSDAF model was most robust regardless of variations in LHI and TVI at both scene level and local level, while it was less computationally efficient than the other models except for one-pair learning; (2) Fit-FC had the highest computing efficiency. It was accurate in predicting reflectance but less accurate than FSDAF and one-pair learning in capturing image structures; (3) One-pair learning had advantages in prediction of large-area land cover change with the capability of preserving image structures. However, it was the least computational efficient model; (4) STARFM was good at predicting phenological change, while it was not suitable for applications of land cover type change; (5) UBDF is not recommended for cases with strong temporal changes or abrupt changes. These findings could provide guidelines for users to select appropriate STIF method for their own applications.

scholarly journals Seasonal and interannual variation in spatio-temporal models for index standardization and phenology studies

2020 ◽  
Vol 77 (5) ◽  
pp. 1879-1892
Author(s):  
James T Thorson ◽  
Charles F Adams ◽  
Elizabeth N Brooks ◽  
Lisa B Eisner ◽  
David G Kimmel ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Cold Pool ◽  
Seasonal Timing ◽  
Limanda Ferruginea ◽  
Resource Managers ◽  
Temporal Models ◽  
Demographic Processes ◽  
Annual Index ◽  
Spatio Temporal ◽  
Index Standardization

Abstract Climate change is rapidly affecting the seasonal timing of spatial demographic processes. Consequently, resource managers require information from models that simultaneously measure seasonal, interannual, and spatial variation. We present a spatio-temporal model that includes annual, seasonal, and spatial variation in density and then highlight two important uses: (i) standardizing data that are spatially unbalanced within multiple seasons and (ii) identifying interannual changes in seasonal timing (phenology) of population processes. We demonstrate these uses with two contrasting case studies: three bottom trawl surveys for yellowtail flounder (Limanda ferruginea) in the Northwest Atlantic Ocean from 1985 to 2017 and pelagic tows for copepodite stage 3+ copepod (Calanus glacialis/marshallae) densities in the eastern Bering Sea from 1993 to 2016. The yellowtail analysis illustrates how data from multiple surveys can be used to infer density hot spots in an area that is not sampled one or more surveys. The copepod analysis assimilates seasonally unbalanced samples to estimate an annual index of the seasonal timing of copepod abundance and identifies a positive correlation between this index and cold-pool extent. We conclude by discussing additional potential uses of seasonal spatio-temporal models and emphasize their ability to identify climate-driven shifts in the seasonal timing of fish movement and ecosystem productivity.

DIFFUSION-DRIVEN INSTABILITIES AND SPATIO-TEMPORAL PATTERNS IN AN AQUATIC PREDATOR–PREY SYSTEM WITH BEDDINGTON–DEANGELIS TYPE FUNCTIONAL RESPONSE

2011 ◽  
Vol 21 (03) ◽  
pp. 663-684 ◽  
Author(s):  
RANJIT KUMAR UPADHYAY ◽  
N. K. THAKUR ◽  
V. RAI
Keyword(s):  
Pattern Formation ◽  
Spatial Heterogeneity ◽  
Collective Motion ◽  
Temporal Dynamics ◽  
Fish Predation ◽  
Building Blocks ◽  
Aquatic System ◽  
Predator Species ◽  
Predator Prey ◽  
Spatio Temporal

Predator–prey communities are building blocks of an ecosystem. Feeding rates reflect interference between predators in several situations, e.g. when predators form a dense colony or perform collective motion in a school, encounter prey in a region of limited size, etc. We perform spatio-temporal dynamics and pattern formation in a model aquatic system in both homogeneous and heterogeneous environments. Zooplanktons are predated by fishes and interfere with individuals of their own community. Numerical simulations are carried out to explore Turing and non-Turing spatial patterns. We also examine the effect of spatial heterogeneity on the spatio-temporal dynamics of the phytoplankton–zooplankton system. The phytoplankton specific growth rate is assumed to be a linear function of the depth of the water body. It is found that the spatio-temporal dynamics of an aquatic system is governed by three important factors: (i) intensity of interference between the zooplankton, (ii) rate of fish predation and (iii) the spatial heterogeneity. In an homogeneous environment, the temporal dynamics of prey and predator species are drastically different. While prey species density evolves chaotically, predator densities execute a regular motion irrespective of the intensity of fish predation. When the spatial heterogeneity is included, the two species oscillate in unison. It has been found that the instability observed in the model aquatic system is diffusion driven and fish predation acts as a regularizing factor. We also observed that spatial heterogeneity stabilizes the system. The idea contained in the paper provides a better understanding of the pattern formation in aquatic systems.

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