Post provided by FRANK BREINER, ARIEL BERGAMINI, MICHAEL NOBIS and ANTOINE GUISAN

Rare Species and their Protection

Erythronium dens-canis L. – a rare and threatened species used for modelling in Switzerland. ©Michael Nobis
Erythronium dens-canis L. – a rare and threatened species used for modelling in Switzerland. ©Michael Nobis

Rare species can be important for ecosystem functioning and there is also a high intrinsic interest to protect them as they are often the most original and unique components of local biodiversity. However, rare species are usually those most threatened with extinction.

In order to help prioritizing conservation efforts, the International Union for Conservation of Nature (IUCN) has published criteria to categorize the status of threatened species, which are then published in Red Lists. Changes in a species’ geographical distribution is one of the several criteria used to assign a threat status. For rare species, however, the exact distribution is often inadequately known. In conservation science, Species Distribution Models (SDMs) have recurrently been used to estimate the potential distribution of rare or insufficiently sampled species.

Ensembles of Small Models (ESMs)

Falcaria vulgaris Bernh. - Another rare species that we used for modelling. ©Ariel Bergamini
Falcaria vulgaris Bernh. – Another rare species that we used for modelling. ©Ariel Bergamini

SDMs have a serious limitation though: they rely on a sufficient number of occurrences to provide reliable predictions. This means they can be difficult to build (due to overfitting) for rare and under-sampled species and related spatial predictions can prove unreliable. So we have a bit of a ‘catch 22’ in that rare species are the ones for which conservationists are most in need of models to compensate their insufficient sampling, but the existing models are unreliable for them due to such small sample size.

In 2010 a promising new strategy was introduced that could overcome these limitations. The authors of this novel strategy used very simple bivariate models (only two predictors at a time per model) and averaged all possible combinations of bivariate models to an ensemble weighted by cross-validated AUC score as a measure of model performance. These ensembles of small models have been shown to perform very well for a single species.

In our paper we tested the ESM strategy thoroughly and found that ESMs always performed better compared to standard SDMs when sample size is small. The fewer occurrences available for modelling the greater the gain in performance – although it’s likely that there is some absolute minimum (still to be defined) under which even small models cannot be built.

ESMs as a Tool for Conservation Management

ESMs can be helpful to improve reliability and accuracy of SDMs for lots of applications in conservation practice. Here we review some of the most promising applications of ESM.

  1. Prospective sampling

An important application of ESMs is to predict the potential habitat of a species and use this information to detect new occurrences in the field. The data which are then sampled using this field-based approach, called prospective sampling, can again be incorporated to an updated SDM. This process can be iteratively repeated until the ‘true distribution’ is known.

Prospective sampling is a suitable approach to get a better understanding of the distribution of rare and under-sampled species which could be used to assign a Red List status. However, the approach has been difficult to initiate when too few observations are available to use the standard SDM approach. ESM offers a solution to this problem and would allow researchers to initiate the iterative procedure. They could then be changed for SDMs when enough observations have been gathered.

A habitat suitability map used for the prospective sampling approach to predict the potential distribution of Leucanthemum halleri (Vitman) Ducommun in the Swiss Alps (orange-black dots show new-found occurrences which were unknown in the database before)
A habitat suitability map used for the prospective sampling approach to predict the potential distribution of Leucanthemum halleri (Vitman) Ducommun in the Swiss Alps (orange-black dots show new-found occurrences which were unknown in the database before)
  1. Climate change impact on communities

Climate change has a strong effect on the loss of biodiversity. ESMs could be used to assess climate change impacts on the distribution of rare species – allowing the species most vulnerable to climate change to be identified. This would be particularly important in the context of modelling the response of future communities to climate change, which is currently hampered by our incapacity to model all or most species in biological communities. Current community modelling efforts usually only include species observed frequently enough to be modelled with standard SDMs. Using ESMs instead of standard SDMs would allow researchers to include all species – including those with low frequencies – and increase the accuracy of the predicted communities.

  1. Invasive species risk assessment

When non-native species start to colonise a new range only little information is known about where the risk of them competing against rare species is highest. Standard SDMs calibrated at the early stages of invasions are likely to be less accurate due of data limitation, which would result in high prediction uncertainties. In such cases ESMs could be used at early stages of invasions, to improve risk assessments for exotic species prone to become invasive when sample size is limited.

  1. Translocation of species

In conservation practice rare species are often translocated to new habitats or for recolonisations to increase the potential of their long-term survival. For such assisted migration it is essential to find a habitat which is suitable for the species to increase chances of success. Suitable habitat of rare species could be best identified using ESMS.

We believe that the contribution of ESMs to nature conservation is important – as shown by ESMs being granted the MCED-award 2015 – and many applications are possible. However, ESM applications likely require more thorough testing and some useful improvements may be developed. In particular, the ESM approach may be implemented using existing statistical frameworks, such as tuning parameters in random forest (RF) or boosted regression trees (BRT) to force them to fit small models (e.g. bivariate) before ensembling, or using generalized or additive models (GLM/GAM) with a multi-model inference framework.

To find out more about Ensembles of Small Models, read our Methods in Ecology and Evolution article ‘Overcoming limitations of modelling rare species by using ensembles of small models’.

This article is part of our Virtual Issue on Endangered Species. All articles in this Virtual Issue are freely available for a limited time.