The future of biodiversity and the functioning of the biosphere in the Anthropocene

Summary:

• Goal: integrate biodiversity data to predict functioning of the biosphere into the future

• Diversity: variety of life on Earth and the complexity of their interactions

  • Focus on quantifying attributes of species:

    • Rarity

    • Body size

  • These attributes predict resilience of ecosystems

• Biosphere: ecological system integrating all living things and their relationships

• Universal signature of complex systems:

  • Inverse size distribution

  • Power law distribution very common: earthquake magnitudes, city sizes

  • Processes that drive power law distributions are being debated

  • Key thing: they provide a baseline for understanding ecosystem state

• Global species abundance distribution (gSAD)

  • Question: how many plant species on Earth?

    • Easy to count common species

    • Rare species are harder

      • Biodiversity depends strongly on distribution of rare species

      • Rare species most vulnerable to extinction

      • Also, having many rare species is insurance against systemic shocks; odds are that some of them will be adapted to the new environment, diseases, etc.

  • Challenge: biodiversity data are in a poor state

    • Broken up into different databases that use different formats/schemas

    • Include many errors

    • BIEN: compiles bio-diversity data on mostly plants and some animals

      • Museum data

      • Vegetation surveys

      • Plant traits and measures

      • Focus is integrating different data sources

      • Version 4.1: 

        • 206 million observations

        • 64m specimens

        • 250k plant species

      • Version 5 will be coming out soon

      • Estimated ~435k total plant species

    • TNRS: Taxonomic name resolution service

    • GNRS: Geographic name resolution service

  • Theory

    • Neutral theory: all species have similar dynamics

      • Predicts a global power law (log series) distribution with a drop at the count of most common species

      • Slope = -1

    • K-niche model:

      • Each species has a preferred environment

      • Limits their spread

      • Predicts power laws (log series) for different environments

    • Central Limit theorem:

      • Many processes interact multiplicative

      • Predicts power law (poisson distribution)

    • Data can be used to validate the theories

      • Different collection methods produce power law distributions

      • Differ from each other in magnitude since some methods are more restrictive than others

      • Slope of on log-log plot: -1.4

      • Supports Central Limit model most strongly

      • 36.5% of species have been observed <=5 times

      • Rate species exist in most climatically stable regions

      • These happen to be the regions that will be most severely affected by climate change

• Application: predict impact of climate change

  • On biodiversity of species

  • Identify areas of planet that are especially vulnerable in terms of biodiversity, carbon storage, water availability

• Distribution of body sizes:

  • Data for plants and mammals shows inverse relationship between size and number of individuals

  • Power law

  • Humans impact largest species and individuals more

    • We hunt/exploit them

  • Result is that many species are becoming smaller; more so on land, less so in ocean

  • 1970-2010 populations of animals on Earth have dropped 52%, disproportionately the larger ones

  • Trees: total number of trees dropped by 46% since start of civilization, disproportionately larger, older trees

  • Argument: largest species have a disproportionate impact on the biosphere

    • Dominate flow of resources, store of carbon, nutrients

    • Central nodes in interaction networks

    • Should be priorities of conservation

• Distribution of the number of individuals within each species: still working on this

• Need a model of the biosphere that can predict how downsizing affects the biosphere

  • The body size and frequency relationships (power law) indicate that the body size of the largest species is most important for the whole ecosystem’s metabolism

  • Madingley model: https://madingley.github.io/

    • Captures distribution of body sizes, species counts, geographically over the world

    • Predicts significant reduction in overall biomass and overall metabolism and global nutrient diffusivity as a result of size reduction

    • Still need to model individual and ecosystem adaptation

• Impact on agriculture:

  • Breeders need to understand which genes are able to make crops live well in modified climates

  • Biobanks have data on close relatives of crop species that live in different climates

  • Can use this to introduce novel genes to make crop species more resilient

  • Can leverage phylogeny to infer crop plants’ ability to deal with various climates (where their relatives live)

  • May be useful to collect data across related species to create more robust plant models

  • Can try the plant species in different current, past and future environments to predict how they respond