Research

Research Overview

CAMBIUM Research Activities focus on the urgent need to predict how biodiversity will respond to climate change, human land use, and extreme events. 

Research will address three major challenges and nine core research questions: 

  1. Predicting potential biodiversity loss
    1. How does the biology of a species interact with existing policy and governance to predict its risk of extinction due to climate change or land-use modification? 
    2. How can information from the phylogenetic and genomic history of a species be incorporated to forecast its risk of population shifts, including invasion and extinction? 
    3. How can climatic changes, including both average weather patterns and extreme weather events, be used to predict forecasts of future species distributions, temporal patterns, and community structures? 
    4. How can innovations in data science improve data integration between ecological and social data to improve forecasting of species geographic distributions and temporal patterns?
  2. Adapting to loss
    1. How can data from species forecasts be used to select resilient crops and agricultural systems for a region, including meeting economic needs? 
    2. How can ecosystem and species forecasts be integrated using a One Health framework to predict the emergence of pathogens and anticipate broader plant, animal, and human health challenges?
  3. Informing action
    1. How can biodiversity-based model outputs be tailored to meet the needs of policymakers and other decision-makers? 
    2. How can we work across sectors and disciplines to examine the trade-offs of different decision-driven scenarios? 
    3. How can the importance of ecosystem composition and function be more effectively communicated for planning and development of new projects?

Core Hypotheses

Addressing this hypothesis requires skills in handling and analyzing large datasets, along with computational and data visualization expertise. A central idea of H1 is that plant and animal communities in already challenging environments (extremely hot, dry, or cold) will struggle to adapt to further environmental changes because they are already living near their physiological limits.
 
To test this hypothesis, we must be able to analyze various climate change projections.  Using a moderate scenario (SSP2-RCP4.5), and incorporating factors like developmental impacts, we can model how close we are to critical thresholds that could drastically alter ecosystem functions, such as productivity and carbon storage.  Preliminary results indicate these thresholds may be reached earlier in tropical regions than in temperate zones, particularly when compared to more severe climate change scenarios.
 
Predicting which species are most at risk from climate change also requires detailed mapping of current and future climate conditions at multiple scales.  While much current research concentrates on changes in average climate, it is increasingly clear that the rising frequency and intensity of extreme events (droughts, fires, floods, and heatwaves) are crucial.  Therefore, we need improved methods to predict these extremes and their impacts on biodiversity.  This involves developing techniques capable of accurately representing the timing and location of extreme events globally, while also providing the local-level detail needed for effective policy decisions.

Zoonotic diseases are a significant threat to human well-being. Emergence is determined by complex human, environmental, and pathogen factors. Precise prediction of an emerging pathogen's time and location is impossible. However, higher-risk areas can be identified for increased surveillance. The geographic range and seasonality of host and vector species determine the risk potential for the emergence and transmission of zoonotic and vector-borne diseases. Invasive species, land use change, and biodiversity loss increase the risk of zoonotic disease emergence. A growing body of literature aims to predict the expansion of disease vectors, such as mosquitoes, worldwide. Coupling species geographic predictions with anthropogenic factors such as population growth and socio-economic variables will enhance public health preparedness for disease emergence. Current models of emerging infections focus on range expansion and to a lesser extent, changing seasonality of risk. Including weather extremes that may alter the bionomics of disease vectors and zoonotic host patterns will increase forecast accuracy.

Addressing this hypothesis involves bridging gaps between research questions and delivering actionable results to decision-makers. Indeed, a core gap that spans each of our research questions is effective methods to deliver actionable research results to decision-makers that are useful for understanding the potential consequences of climate change, and other drivers of extinction risk and infectious disease spread used to inform policy. Effective forecasting must meet several basic criteria: it must be 1) at a spatial resolution that meets the scale of decision-making, 2) at a temporal resolution that is actionable by decision-makers seeking to prevent or mitigate a predicted outcome, and 3) at an acceptable level of certainty that decision-makers feel comfortable making choices that may involve tradeoffs. No one-size-fits-all spatial and temporal scale for biodiversity data will be useful for all decision-makers. At international scales, decision-makers may require data at a mix of scales, e.g. specific data on a species’ population status and trend and general data on its global distribution and habitat locations, to make decisions about when it should be listed under CITES, CMS, or the IUCN Red List. At national and local scales, the same level of species trend data but more specific habitat and distribution data may be needed to decide about species protection under national laws and where these protections apply. Scientific uncertainty and how it is presented to decision-makers interacts with spatial and temporal scale; higher levels of uncertainty may be more acceptable to decision-makers for a global forecast with a longer time horizon than a near-term local forecast. Through research led by CAMBIUM faculty and the program’s case studies course, students will engage in transdisciplinary research to develop datasets that address these three challenges at the interface of science and policy.