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Field Collection and Laboratory Maintenance of Canopy-Forming Giant Kelp to Facilitate Restoration

Field Collection and Laboratory Maintenance of Canopy-Forming Giant Kelp to Facilitate Restoration

Published on: June 7, 2024

Advances and challenges in ecological connectivity science.

Amanda R Liczner¹, Richard Pither², Joseph R Bennett³

¹Okanagan Institute for Biodiversity, Resilience and Ecosystem Services University of British Columbia Kelowna British Columbia Canada.

Ecology and Evolution

|September 3, 2024

Summary

Ecological connectivity science advances biodiversity conservation. New models improve understanding of species movement and climate change impacts, guiding evidence-based conservation actions.

Keywords:

biological realism climate change connectivity conservation corridors directional movement multi‐species

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Area of Science:

Ecology
Conservation Biology
Spatial Modeling

Background:

Biodiversity loss is a critical global issue requiring effective conservation strategies.
Ecological connectivity science has advanced significantly, providing tools for conservation.
Sophisticated modeling of functional connectivity is increasingly feasible due to data and computing power.

Purpose of the Study:

To review key advances in connectivity science over the past decade.
To identify remaining challenges in modeling and applying connectivity.
To inform evidence-based conservation actions for biodiversity.

Main Methods:

Review of recent literature on connectivity modeling.
Discussion of advancements in biological realism of models (e.g., movement, population parameters, climate change).
Exploration of single-species vs. multi-species modeling approaches.

Main Results:

Connectivity models are becoming more biologically realistic and sophisticated.
Increased data and computational power enable advanced functional connectivity modeling.
Key challenges remain in accounting for uncertainty and ensuring transparency.

Conclusions:

Advances in connectivity science offer powerful tools for biodiversity conservation.
Future research should focus on integrating biological realism, addressing uncertainty, and enhancing reproducibility.
Choosing appropriate model complexity is crucial for effective conservation decisions.