Climate Conditions and Biodiversity Decline: Impact Assessment

Introduction

Climate change impacts on biodiversity are projected to increase in magnitude and pervasiveness as CO₂ levels and temperatures continue to rise, and extreme events (e.g., heat and storms) increase in frequency and intensity (IPCC, 2007). A range of methods and approaches are being employed to predict the impacts of climate change on biodiversity including historical trends and relationships, experiments, and model projections. As these methods have become more sophisticated, so has our understanding of projected impacts, particularly on aquatic and marine systems, which have been less well studied compared to terrestrial systems. Although there are many ways to categorize the modelling approaches used to assess potential impacts of climate change on biodiversity, most can be described as either empirical (correlative) or process-based (mechanistic). Projections of species distributions often come from empirical models that relate observed occurrences to current or historical climate conditions, and predict future distributions using projected changes in the geographic distributions of abiotic variables (e.g., temperature). These models, which are often referred to as climate envelope, niche, or species distribution models, have the advantage of being relatively easy to apply to large numbers of species, and have been used to project potential shifts in areas of climatic suitability for plants and animals at varying scales across the United States (Staudinger et al., 2013). Similar empirical models have been used to project changes in biomes and vegetation types (Rehfeldt et al., 2012). Despite their flexibility, empirical models generally do not directly model biotic interactions (e.g., competition), account for evolution, or address dispersal; these limitations can lead to an overestimation of the ability of species to track climatic changes, and an underestimation of extinction rates. In contrast, process-based models are designed to specifically account for a number of the mechanisms that determine species distributions or vegetation patterns, and can simulate physiological responses, population processes, dispersal, ecosystem functions, and plant growth. Some examples include spatially explicit, individual-based population models, dynamic global vegetation models (DGVM), and forest gap models (Staudinger et al., 2013). There have been several recent efforts to integrate individual empirical and process-based models to ensembles of such models to improve projections of the impacts of climate change on various components of biodiversity (Araujo & New, 2007).

These include efforts to:

• 1.

  Account for dispersal or movement in empirical models of changes in species or population distributions.

• 2.

  Combine metapopulation models with projected shifts in climatic suitability.

• 3.

  Integrate multiple mechanisms into projected changes in the distribution of marine species.

• 4.

  Combine niche models with physiological mechanistic models.

These new and increasingly sophisticated methods of linking niche models, trophic models, dynamic vegetation models, and global climate models with socioeconomic scenarios are increasing our abilities to predict and evaluate future impacts of climate change on biodiversity (Staudinger et al., 2013). Biodiversity has a significant impact on ecosystems productivity and stability, and on the services they generate. Specifically: crop genetic diversity increases the yield of commercial agricultural crops; tree species diversity enhances and/or stabilises wood production in plantations; plant species diversity produces more fodder in grasslands; fish species diversity is associated with more stable catches. Furthermore, biodiversity makes ecosystem productivity more resilient to climate extremes (Dilys et al., 2019).