Exploring Population Responses to Environmental Change When There’s Never Enough Data

Post provided by Bethan Hindle

Understanding Population Responses to Environmental Change

Rapid climatic change has increased interest about how populations respond to environmental change. This has broad applications, for example in the management of endangered and economically important species, the control of harmful species, and the spread of disease. At the population level changes in abundance are driven by changes in vital rates, such as survival and fecundity. So studies that track individual survival and reproduction over time can provide useful insights into the drivers of such changes. They allow us to make future population level predictions on things like abundance, extinction risk and evolutionary strategies.

Archbold Biological Station - site of numerous long-term demographic studies, including that of Eryngium cuneifolium used in this paper. ©Reed Bowman

Archbold Biological Station – site of numerous long-term demographic studies, including that of Eryngium cuneifolium used in this paper. ©Reed Bowman

Predicting the future isn’t a simple task though. Anyone whose washing has got soaked through after the weather forecast suggested the day would be dry and sunny will know that (though the accuracy of short term weather forecasts has increased dramatically in recent years). Ideally, if we want to predict what will happen to populations as their environment changes, we would identify the drivers of variation in their survival and reproduction. We do this by asking questions like ‘are years of low survival associated with high rainfall?’ But, this is not a simple task; identifying drivers and the time periods over which they act and accurately estimating their effects requires long-term demographic data.   Continue reading

Capturing the Contribution of Rare and Common Species to Turnover: A Multi-Site Version of Generalised Dissimilarity Modelling

Post provided by Guillaume Latombe and Melodie A. McGeoch

Understanding how biodiversity is distributed and its relationship with the environment is crucial for conservation assessment. It also helps us to predict impacts of environmental changes and design appropriate management plans. Biodiversity across a network of local sites is typically described using three components:

  1. alpha (α) diversity, the average number of species in each specific site of the study area
  2. beta (β) diversity, the difference in species composition between sites
  3. gamma (γ) diversity, the total number of species in the study area.
Two tawny frogmouths, a species native to Australia. ©Marie Henriksen.

Two tawny frogmouths, a species native to Australia. ©Marie Henriksen.

Despite the many insights provided by the combination of alpha, beta and gamma diversity, the ability to describe species turnover has been limited by the fact that they do not consider more than two sites at a time. For more than two sites, the average beta diversity is typically used (multi-site measures have also been developed, but suffer shortcomings, including difficulties of interpretation). This makes it difficult for researchers to determine the likely environmental drivers of species turnover.

We have developed a new method that combines two pre-existing advances, zeta diversity and generalised dissimilarity modelling (both explained below). Our method allows the differences in the contributions of rare versus common species to be modelled to better understand what drives biodiversity responses to environmental gradients. Continue reading

Predicting Disease Outbreaks Using Environmental Changes

Below is a press release about the Methods paper ‘Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever‘ taken from the University College London.

The multimammate rat (Mastomys natalensis) transmits Lassa virus to humans. ©Kelly, et al.

The multimammate rat transmits Lassa virus to humans. ©Kelly, et al.

A model that predicts outbreaks of zoonotic diseases – those originating in livestock or wildlife such as Ebola and Zika – based on changes in climate, population growth and land use has been developed by a UCL-led team of researchers.

“This model is a major improvement in our understanding of the spread of diseases from animals to people. We hope it can be used to help communities prepare and respond to disease outbreaks, as well as to make decisions about environmental change factors that may be within their control,” said lead author Professor Kate Jones, UCL Genetics, Evolution & Environment and the Zoological Society of London. Continue reading