BioTRaCE

Simons Collaboration on
Biodiversity Trajectories and Causal understanding of Ecoevolutionary dynamics

I live in a Global Biodiversity Hotspot and one of the most botanically diverse regions of the world. My house is a stone’s throw away from Table Mountain National Park, which is home to almost 3000 vascular plant species.

Unfortunately, this is changing.

In 2017, some members of this team and I published the first documented evidence of climate-change driven diversity loss in this region.

Since then, the region has experienced a 3-year megadrought, multiple floods from extreme heavy rainfall, and aseasonal fires.

This is a photo I took near my house a week or two ago. The combination of an out-of-season fire and barely any rain this wet season has resulted in 80-90% mortality in these large fire-resistant shrubs.

What is happening to the rest of the thousands of less conspicuous species we’ll only know once we’ve censused and analysed our permanent vegetation survey plots in the area.

This uncertain state and trajectory of biodiversity and associated contributions to people is not unique to my home. It is a global crisis.

And while we may know many of the drivers, we lack the ability to confidently track and forecast their impacts or future trajectories under different intervention scenarios.

We will develop a unified framework for iteratively testing theory and predicting biodiversity dynamics and Nature’s Contributions to People across space and time.

[Or drop this slide and go directly into the 3 core challenges?]

[Or present something like this and say “We will develop a unified framework for iteratively testing theory and predicting biodiversity dynamics and Nature’s Contributions to People across space and time.”?]

Three Core Challenges

Biodiversity

Observation

Inference

Developing the ability to forecast biodiversity trajectories with confidence requires addressing three core challenges…

Firstly, we need to reduce the innate variation and complexity of biodiversity if we are to explore mechanistic processes and linkages.

• For example, the Cape Floristic Region is home to >9000 vascular plant species and many more that are yet to be described. We cannot hope to directly model every single one of them.

Secondly, we need to overcome challenges associated with patchy biodiversity observations in space and time.

• This visualization of all species locality records in Africa curated by the Global Biodiversity Information Facility reveals many dark parts on the map. The record is even patchier when explored over time. One cannot model trajectories without data.

Third, historically, we have only been able to test causal relationships with randomised control trials.

• These are logistically and financially constrained, and even some of the largest and longest running, such as the Park Grass Experiment shown here, are only up to the size of a small farm.
• This means that much of our current theory has only been properly tested at limited spatial and temporal extents, and there are many reasons they may not hold at broader scales as assumptions and the relative importance of processes change.

Three Solutions

Diversity

Trait-Based Ecology

Mechanistic links between scales from organism to ecosystem and Nature’s Contributions to People

Fortunately, the past few decades and recent developments are allowing us to overcome these challenges.

Firstly, trait-based ecology allows us to simplify the complexity of biodiversity and explore mechanistic links and processes that scale from individual organisms to ecosystems and Nature’s Contributions to People, such as carbon sequestration, provision of clean water, or even human well-being.

Three Solutions

Observation

Satellite Remote Sensing

Provides repeated observations at scales from metres to the globe

Secondly, satellite remote sensing provides repeated observations at scales from metres to the globe, which when paired with ground observations can tell us a lot about the state and trajectory of various facets of biodiversity.

Three Solutions

Inference

Modern Causal Statistics

Allows us to test causation at the scale of observations

Third, burgeoning methods in modern causal statistics are freeing us from the constraints of randomised control trials and allowing us to infer causation at scales that match our observations and application.

Combining the solutions

Trait maps from field surveys and imaging spectroscopy

Our team have been working on these solutions and the potential to combine them to create something transformational for biodiversity theory, observation and forecasting.

Firstly, members of our team have been leading efforts to map plant traits from imaging spectroscopy (hyperspectral remote sensing) groundtruthed with field data.

• Here we see false colour composites of 18 NEON landscapes in the US with three traits, Phenolics, Chlorophyll and Leaf Mass per unit Area mapped to red, green and blue. As you can see, these images reveal clear variation in these traits within and between landscapes, revealing the legacy of land-use or ecological histories.

Our team members have also been involved in the development of hyperspectral satellite missions (such as NASA’s Eagle and ESA’s CHIME), that are to be launched in the next 2-3 years.

Combining the solutions

Trait time-series from multispectral satellites

Time-series of traits from hyperspectral satellites will be transformational for biodiversity science and monitoring, but existing airborne and satellite data are patchy in space and time.

To prepare for regular hyperspectral satellite data with continuous coverage, we will use field data and trait maps from airborne campaigns to calibrate and develop trait time-series from decades of records from existing multispectral satellites such as Landsat or Sentinel.

Combining the solutions

Model and forecast trait time-series

We can then develop iterative forecasts of these traits that update as new imagery become available.

Initially, the new data will be from multispectral satellites, but we’ll switch to hyperspectral satellites when the data become available, which should be during the project.

This allows us to leverage decades of existing data, but then refine the models as the higher quality hyperspectral data become available.

Combining the solutions

Use counterfactual models and forecasts to test causal drivers and theory

[This is not the final graphic. I want to ask Phil’s designer if she can make a version with better “intervention” forecasts.]

The models and iterative forecasts can serve multiple purposes.

• Firstly, they can be used to inform the management of biodiversity and Nature’s Contributions to People by detecting and attributing change as it happens or exploring potential outcomes under different decision scenarios.
• Secondly, we can use them for causal inference, by intervening on key variables or adding or altering mechanistic processes in the model to explore counterfactual scenarios. These hypothetical “what if” situations allow us to figure out cause-and-effect relationships by comparing reality to alternative outcomes.

A common framework to unify theory!

Describing eco-evolutionary dynamics by harmonizing existing theory in a trait-based framework

Synthesizing theory across physiology, population biology, evolutionary biology, community ecology, ecosystem ecology, and global ecology.

We’ll also apply… [Help Cory?]

We’ve structured the project around 4 working groups, each with their own set of work packages, questions and responsibilities. Each working group will be making their own novel advances, but together they form an iterative learning cycle that allows us to learn and share across groups.

The Theory and Synthesis group involves the entire team and others and is responsible for both guiding the other groups and iteratively reviewing, synthesizing and redistributing knowledge gained within the project, while also engaging with the needs of biodiversity management and policy decision-makers.

Working group 2 will focus on developing rich biodiversity and environmental time-series from satellite and airborne imagery, field data and expert elicitation. This is the fuel for working group 3 and 4.

Working group 3 is focused on developing iterative ecological forecasting models and causal analysis to detect and attribute drivers of biodiversity change and test theory.

Working group 4 will use the data and findings from WG 2 and 3 in a Trait Driver Framework to reframe existing theory in terms of traits and test their utility for biodiversity prediction (again Cory - Help?).

Our Team

• Remote Sensing of Biodiversity
• Statistical Ecology
• Theoretical Ecology
• Community Ecology
• Trait Ecology
• Landscape Ecology
• Hydroclimatology
• People and Nature
• Conservation Planning
• Science and Policy
• and more…

Our team represent three of the 5 MTEs and have data and experience in many others and include the broad range of skills necessary to complete this project, from field to theoretical ecology to remote sensing and conservation policy.

What success looks like?

A framework for iterative testing and integration of theory informed by ongoing global observations

We will develop an open source framework and tools for iterative, and ideally automated, testing and integration of theory informed by ongoing global observations.

Once developed, the learning framework and tools would allow further development and testing of theory well beyond the lifetime of the project.

[Not sure about these chatGPT-generated graphics. I’d like to put more thought into what they’re showing. Suggestions welcome!]

What success looks like?

Theory-informed iterative forecasting of biodiversity and NCP to inform decision making

Of course our main goal behind developing the learning framework is to identify and test existing theory and develop new theory to improve our ability to forecast trajectories for biodiversity and NCP. So success would be to have theory-informed iterative forecasts up and running.

[Not sure about these chatGPT-generated graphics. I’d like to put more thought into what they’re showing. Suggestions welcome!]

What success looks like?

Better managed biodiversity and Nature’s Contributions to People

And it goes without saying that we’d like to see better managed biodiversity and Nature’s Contributions to People.

What success looks like?

A framework for iterative testing and integration of theory informed by ongoing global observations

Theory-informed iterative forecasting of biodiversity and NCP to inform decision making

Better managed biodiversity and Nature’s Contributions to People

[Closing slide]

What success looks like?

That’s it!

Struggling a bit with “What success looks like?”

BioTraCE

Providing a unified, testable framework for predicting biodiversity dynamics and Nature’s Contributions to People across space and time.

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