Lecture 8 - 3444

Landscape Genomics BIO3444 - Lecture 8

Spatial Variability

  • Definition: Differences in ecological conditions, such as habitat types, resources, or environmental factors, that occur in different areas within an ecosystem or landscape.

Source-Sink Dynamics

  • Concept: Different habitats within a landscape contribute differently to the overall persistence of a population.

  • Movement: Individuals move between areas of varying habitat quality, affecting population structure and sustainability.

Source Habitats

  • Areas where local environmental conditions are favorable for species, leading to a surplus of individuals (positive population growth rates).

Sink Habitats

  • Areas where local environmental conditions are suboptimal; the population cannot sustain itself without influx from source areas (negative population growth rates).

  • Importance:

    • Movement from source habitats to sink habitats maintains populations in sinks and ensures gene flow across the landscape.

Population Stability

  • Ensures less productive habitats are maintained through dispersal.

  • Connectivity prevents population collapse when habitats temporarily become unsuitable due to environmental changes.

Adaptation and Evolution

  • Dispersal leads to gene flow that affects evolutionary processes, maintaining genetic diversity and promoting adaptation in sink populations.

Conservation and Management

  • Protect source habitats while maintaining connectivity between source and sink areas.

Microhabitat

  • Habitat within a habitat, possessing unique properties where new variations of life can thrive due to specific conditions.

Landscape Patterns

  • Various landscape structures (e.g., patchwork of forest, grassland, urban areas) create a mosaic affecting species movement, genetic flow, and population connectivity.

Temporal Variability

  • Definition: Changes in ecological conditions over time, from daily fluctuations to long-term trends like climate change.

  • Significance: Understanding ecological resilience—how ecosystems respond and recover from disturbances.

  • Impact on Evolution: Drives evolutionary pressures, prompting adaptations and changes in population dynamics over time.

Integrating Spatial and Temporal Variability

  • Ecological processes depend on both spatial and temporal scales.

  • Species must adapt to environmental fluctuations over space and time.

  • Understanding variability aids in designing effective conservation strategies.

  • Including variability in ecological models helps predict ecosystem responses to future changes.

Landscape Ecology

  • Definition: Scientific study exploring the relationships between spatial patterns and ecological processes at multiple scales, encompassing both space and time.

Categorizing Landscapes

GIS (Geographic Information Systems)

  • Integral in transitioning from categorical to continuous analysis in landscape ecology.

  • Allows integration and analysis of spatial data, enabling researchers to create detailed models of ecological gradients across large areas.

Remote Sensing

  • Utilizes satellite imagery and aerial photography to provide extensive, high-resolution environmental data (e.g., vegetation cover, temperature, topography).

  • Facilitates the creation of continuous landscape models reflecting real-world conditions.

Combining GIS and Remote Sensing

  • Enables mapping and analyzing environmental gradients.

  • Digital Elevation Models (DEMs) and vegetation indices (NDVI) can be layered to understand ecological variability.

Patch-Mosaic Model

  • Landscapes viewed as a mosaic of discrete patches embedded in a matrix.

  • Each patch represents a relatively homogenous area differing in composition, structure, or function from its surroundings.

  • Matrix: The background or dominant landscape type that connects these patches.

  • Utility: Simplifies complex ecological patterns for study, facilitating understanding of species resource use and habitat fragmentation effects.

  • Limitations: May miss nuanced variations within patches and continuous interactions across landscapes.

Gradient Paradigm

  • Treats landscapes as dynamic surfaces where attributes change gradually and continuously.

  • Used for modeling species distribution and habitat suitability by mapping fluctuations like temperature or vegetation across a landscape.

Exurban Development

  • Low-density housing in landscapes dominated by native vegetation, impacting biodiversity and ecological integrity.

Landscape Genomics

  • Landscape Genetics: Analyzes how landscape features affect genetic variation using a limited number of markers.

  • Landscape Genomics: Expands this analysis to genome-wide data, providing insights into adaptive processes and genetic basis of local adaptations in response to environmental pressures.

Applications of Landscape Genomics

  • Identify Evolutionarily Significant Units, map adaptive genetic variation, understand connectivity and gene flow, predict population resilience to climate change, and improve habitat management and restoration efforts.

Challenges and Limitations

  • Data volume and complexity, computational resources, bioinformatic challenges, and linking genetic variation to function.

Sampling Strategies

  • Random Sampling: Scattered or clustered sampling from across populations based on environmental or genetic factors.

  • Stratified Sampling: Requires extensive biological and environmental information for targeted species.

Spatial Autocorrelation

  • Degree to which spatial data points are correlated based on arrangement.

  • Positive Autocorrelation: Nearby locations have similar values.

  • Negative Autocorrelation: Nearby locations have contrasting values.

  • No Autocorrelation: Values at one location are independent of nearby values.

Statistical Methods

  • Moran’s I: Measures spatial autocorrelation (-1 to +1).

  • Geary’s C: Ranges from 0 to 2 to assess spatial distribution.

  • Mantel Test: Correlates genetic and geographic distances, with varying outcomes indicating genetic similarity based on spatial proximity.

Space, Environment, and Gene Flow

  • Examines balances between population genetic divergence and gene flow, alongside adaptive trait evolution influenced by selection pressures.

Isolation Mechanisms

Isolation by Dispersal Limitation (IBDL)

  • Reduction in gene flow with increasing geographic distance, leading to genetic differentiation patterns.

Isolation by Adaptation (IBA)

  • Local adaptation to distinct environmental conditions limits gene flow; genetic divergence based on ecological differences rather than distance.

Isolation by Colonization (IBC)

  • Founder effects and priority effects shape genetic structure; initial colonizers' adaptation can limit future gene flow.

Natural Landscapes Analysis

  • Evaluates correlation between genetic variation and geographic/ecological distances.

  • Isolation by Distance (IBD): Increasing genetic differentiation at neutral loci with geographic distance.

  • Isolation by Environment (IBE): Genetic differentiation at loci under selection correlated with ecological rather than geographic distance.

  • Mixed Patterns: Genetic structures may stem from a combination of IBDL and IBA scenarios.

Case Study: Arizona Sky Islands

  • Environment: Diverse landscapes featuring isolated habitats impacting species adaptation.

  • Adaptive Loci Tests: Analyzes genetic structures and gene flow dynamics related to environmental conditions.

Applications of Landscape Genomics

  • Implement multi-species conservation strategies informed by genomic diversity and environmental contexts.