scholarly journals Classical metapopulation dynamics and eco-evolutionary feedbacks in dendritic networks

2015 ◽  
Author(s):  
Emanuel A. Fronhofer ◽  
Florian Altermatt
Keyword(s):  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Extinction Risk ◽  
Network Connectivity ◽  
Structured Populations ◽  
Metapopulation Dynamics ◽  
Temporal Correlations ◽  
Dendritic Networks ◽  
Spatio Temporal ◽  
The Impact

Eco-evolutionary dynamics are now recognized to be highly relevant for population and community dynamics. However, the impact of evolutionary dynamics on spatial patterns, such as the occurrence of classical metapopulation dynamics, is less well appreciated. Here, we analyse the evolutionary consequences of spatial network connectivity and topology for dispersal strategies and quantify the eco-evolutionary feedback in terms of altered classical metapopulation dynamics. We find that network properties, such as topology and connectivity, lead to predictable spatio-temporal correlations in fitness expectations. These spatio-temporally stable fitness patterns heavily impact evolutionarily stable dispersal strategies and lead to eco-evolutionary feedbacks on landscape level metrics, such as the number of occupied patches, the number of extinctions and recolonizations as well as metapopulation extinction risk and genetic structure. Our model predicts that classical metapopulation dynamics are more likely to occur in dendritic networks, and especially in riverine systems, compared to other types of landscape configurations. As it remains debated whether classical metapopulation dynamics are likely to occur in nature at all, our work provides an important conceptual advance for understanding the occurrence of classical metapopulation dynamics which has implications for conservation and management of spatially structured populations.

scholarly journals The importance of species interactions in eco-evolutionary community dynamics under climate change

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna Åkesson ◽  
Alva Curtsdotter ◽  
Anna Eklöf ◽  
Bo Ebenman ◽  
Jon Norberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Negative Impact ◽  
Trophic Levels ◽  
Temperature Dependent ◽  
Modeling Framework ◽  
Impact Of Climate Change ◽  
The Impact

AbstractEco-evolutionary dynamics are essential in shaping the biological response of communities to ongoing climate change. Here we develop a spatially explicit eco-evolutionary framework which features more detailed species interactions, integrating evolution and dispersal. We include species interactions within and between trophic levels, and additionally, we incorporate the feature that species’ interspecific competition might change due to increasing temperatures and affect the impact of climate change on ecological communities. Our modeling framework captures previously reported ecological responses to climate change, and also reveals two key results. First, interactions between trophic levels as well as temperature-dependent competition within a trophic level mitigate the negative impact of climate change on biodiversity, emphasizing the importance of understanding biotic interactions in shaping climate change impact. Second, our trait-based perspective reveals a strong positive relationship between the within-community variation in preferred temperatures and the capacity to respond to climate change. Temperature-dependent competition consistently results both in higher trait variation and more responsive communities to altered climatic conditions. Our study demonstrates the importance of species interactions in an eco-evolutionary setting, further expanding our knowledge of the interplay between ecological and evolutionary processes.

Estimating spatio-temporal dynamics of size-structured populations

2014 ◽  
Vol 71 (2) ◽  
pp. 326-336 ◽  
Author(s):  
Kasper Kristensen ◽  
Uffe Høgsbro Thygesen ◽  
Ken Haste Andersen ◽  
Jan E. Beyer
Keyword(s):  
Gadus Morhua ◽  
Temporal Dynamics ◽  
Size Structure ◽  
Stock Assessment ◽  
Computational Cost ◽  
Structured Populations ◽  
Spatial Distributions ◽  
Temporal Correlations ◽  
Spatio Temporal ◽  
High Computational Cost

Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with simple stock dynamics to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod (Gadus morhua) population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering of individuals with similar size. The model estimates growth, mortality, and reproduction, after which any aspect of size structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: (i) tracking the spatial movements of a single cohort through time and (ii) predicting the risk of bycatch of undersized individuals. The method demonstrates that it is possible to combine stock assessment and spatio-temporal dynamics; however, this comes at a high computational cost. The model can be extended by increasing its ecological fidelity, although computational feasibility eventually becomes limiting.

scholarly journals The importance of species interactions in spatially explicit eco-evolutionary community dynamics under climate change

2020 ◽  
Author(s):  
Anna Åkesson ◽  
Alva Curtsdotter ◽  
Anna Eklöf ◽  
Bo Ebenman ◽  
Jon Norberg ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Interactions ◽  
Community Dynamics ◽  
Evolutionary Dynamics ◽  
Negative Relationship ◽  
Trophic Levels ◽  
Spatially Explicit ◽  
Modeling Framework ◽  
Impact Of Climate Change ◽  
The Impact

AbstractEco-evolutionary dynamics are essential in shaping the biological response of communities to ongoing climate change. Here we develop a spatially explicit eco-evolutionary framework which integrates evolution, dispersal, and species interactions within and between trophic levels. This allows us to analyze how these processes interact to shape species- and community-level dynamics under climate change. Additionally, we incorporate the heretofore unexplored feature that species interactions themselves might change due to increasing temperatures and affect the impact of climate change on ecological communities. The new modeling framework captures previously reported ecological responses to climate change, and also reveals two new key results. First, interactions between trophic levels as well as temperature-dependent competition within a trophic level mitigate the negative impact of climate change on global biodiversity, emphasizing the importance of understanding biotic interactions in shaping climate change impact. Second, using a trait-based perspective, we found a strong negative relationship between the within-community variation in preferred temperatures and the capacity to respond to climate change. Communities resulting from different ecological interaction structures form distinct clusters along this relationship, but varying species’ abilities to disperse and adapt to new temperatures leave it unaffected.

scholarly journals The importance and adaptive value of life history evolution for metapopulation dynamics

2017 ◽  
Author(s):  
Dries Bonte ◽  
Quinten Bafort
Keyword(s):  
Life History ◽  
Evolutionary Dynamics ◽  
Local Density ◽  
Life History Evolution ◽  
Individual Performance ◽  
Structured Populations ◽  
Metapopulation Dynamics ◽  
Systems Modelling ◽  
Metapopulation Persistence ◽  
History Evolution

1. The spatial configuration and size of patches influence metapopulation dynamics by altering colonisation-extinction dynamics and local density-dependency. This spatial forcing as determined by the metapopulation typology then imposes strong selection pressures on life history traits, which will in turn feedback on the ecological metapopulation dynamics. Given the relevance of metapopulation persistence for biological conservation, and the potential rescuing role of evolution, a firm understanding of the relevance of these eco-evolutionary processes is essential. 2. We here follow a systems modelling approach to quantify the importance of spatial forcing and experimentally observed life history evolution for metapopulation demography as quantified by (meta)population size and variability. We therefore developed an individual based model matching an earlier experimental evolution with spider mites to perform virtual translocation and invasion experiments that would have been otherwise impossible to conduct. 3. We show that (1) metapopulation demography is more affected by spatial forcing than by life history evolution, but that life history evolution contributes substantially to changes in local and especially metapopulation-level population sizes, (2) extinction rates are minimised by evolution in classical metapopulations, and (3) evolution is optimising individual performance in metapopulations when considering the importance of more cryptic stress resistance evolution. 4. Ecological systems modelling opens up a promising avenue to quantify the importance of eco-evolutionary feedbacks for larger-scale population dynamics. Metapopulation sizes are especially impacted by evolution but its variability is mainly determined by the spatial forcing. 5. Eco-evolutionary dynamics can increase the persistence of classical metapopulations. The maintenance of evolutionary dynamics in spatially structured populations is thus not only essential in the face of environmental change; it also generates feedbacks that impact metapopulation persistence.

scholarly journals A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields

2021 ◽  
Author(s):  
Kevin Bulthuis ◽  
Eric Y. Larour
Keyword(s):  
Random Fields ◽  
Ice Sheet ◽  
The Finite Element Method ◽  
Sources Of Uncertainty ◽  
Temporal Correlations ◽  
Matérn Covariance ◽  
Spatio Temporal ◽  
Matern Covariance ◽  
The Impact

Abstract. Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model-based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis involves first the (probabilistic) characterization of the sources of uncertainty followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio(-temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties in ice-sheet models, we propose in this paper to represent them as Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. Samples from a Gaussian random field with Matérn covariance function can be generated efficiently by solving a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite element method results in a sparse approximation known as a Gaussian Markov random field. We solve this equation efficiently using the finite element method within the Ice-sheet and Sea-level System Model (ISSM). In addition, spatio-temporal samples can be generated by combining an autoregressive temporal model and the Matérn field. The implementation is tested on a set of synthetic experiments to verify that it captures well the desired spatial and temporal correlations. Finally, we demonstrate the interest of this sampling capability in an illustration concerned with assessing the impact of various sources of uncertainties on the Pine Island Glacier, West Antarctica. We find that both larger spatial and temporal correlations lengths will likely result in increased uncertainty in the projections.

Side information interpolation algorithm based on spatio-temporal correlations at decoder

2013 ◽  
Vol 32 (8) ◽  
pp. 2324-2327
Author(s):  
Feng-qin WANG ◽  
Xiao-lei CHEN ◽  
Yan CHEN
Keyword(s):  
Side Information ◽  
Interpolation Algorithm ◽  
Temporal Correlations ◽  
Spatio Temporal

scholarly journals Endemic Juniperus Montane Species Facing Extinction Risk under Climate Change in Southwest China: Integrative Approach for Conservation Assessment and Prioritization

Biology ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 63
Author(s):  
Mohammed A. Dakhil ◽  
Marwa Waseem A. Halmy ◽  
Walaa A. Hassan ◽  
Ali El-Keblawy ◽  
Kaiwen Pan ◽  
...  
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Extinction Risk ◽  
In Situ Conservation ◽  
Integrative Approach ◽  
Conservation Assessment ◽  
Distribution Models ◽  
Area Of Occupancy ◽  
Annual Range ◽  
The Impact

Climate change is an important driver of biodiversity loss and extinction of endemic montane species. In China, three endemic Juniperus spp. (Juniperuspingii var. pingii, J.tibetica, and J.komarovii) are threatened and subjected to the risk of extinction. This study aimed to predict the potential distribution of these three Juniperus species under climate change and dispersal scenarios, to identify critical drivers explaining their potential distributions, to assess the extinction risk by estimating the loss percentage in their area of occupancy (AOO), and to identify priority areas for their conservation in China. We used ensemble modeling to evaluate the impact of climate change and project AOO. Our results revealed that the projected AOOs followed a similar trend in the three Juniperus species, which predicted an entire loss of their suitable habitats under both climate and dispersal scenarios. Temperature annual range and isothermality were the most critical key variables explaining the potential distribution of these three Juniperus species; they contribute by 16–56.1% and 20.4–38.3%, respectively. Accounting for the use of different thresholds provides a balanced approach for species distribution models’ applications in conservation assessment when the goal is to assess potential climatic suitability in new geographical areas. Therefore, south Sichuan and north Yunnan could be considered important priority conservation areas for in situ conservation and search for unknown populations of these three Juniperus species.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Linear Response of General Observables in Spiking Neuronal Network Models

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 155
Author(s):  
Bruno Cessac ◽  
Ignacio Ampuero ◽  
Rodrigo Cofré
Keyword(s):  
Linear Response ◽  
Network Connectivity ◽  
Network Models ◽  
General Context ◽  
Collective Effect ◽  
Neuronal Dynamics ◽  
The Past ◽  
Spatio Temporal ◽  
External Stimuli ◽  
Spike Dynamics

We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.

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