When using phylogenies in spatial conservation prioritisation, we need to link the phylogeny with distribution data. Increasingly, distribution data is used to predict where species occur across the landscape using a species distribution model (SDM). SDMs are currently underused in conservation, but have great potential for a variety of applications from threatened species management to conservation planning. Our recent paper shows how to use SDMs with a phylogeny in spatial conservation planning (this method could also be used for a variety of applications linking phylogenies and SDMs).

An SDM models the response of a species to a set of predictor variables (usually environmental variables). The model can be extended across a landscape with a probability of occurrence of species in grid cells**. The external branches (tips) of the phylogeny correspond to a particular taxon (let’s assume we have a species-level tree). Therefore, each external branch can simply be the probability of that species occurring in each cell (a,b,c,e,f in figure above). Now, for the internal branches. Continue reading Linking species distribution models (SDM) and phylogenies →

Read more from the New Scientist:

Tree of bird life could solve Noah’s Ark problem

How do we best preserve the world’s remaining biodiversity? That was the topic of a conference I attended last week at the Royal Society in London on ‘Phylogeny, extinction risk and conservation’.  The two-day conference included a range of interesting presentations on global to regional conservation efforts.

Obviously the extinction story can be a depressing one—the Yangtze River Dolphin is most likely extinct and one in five plant species are threatened with extinction. However, even given the looming threats to biodiversity, there is a huge effort underway to make informed decisions about how to prevent further losses.
Continue reading Keeping the tree of life intact →

We have a new paper out at MEE. It is a guide on how to use Joint Species Distribution Models (JSDMs) originally known (to us) as Multivariate Probit models (MVP), a name which thankfully didn’t stick.

Without delving into the details, this is a way to use species co-occurrence data when modelling species distributions. Why is this important? Continue reading Co-occurrence within an SDM →

Locating areas where species will likely persist in future climate changes has recently become a conservation priority. How do we find these areas? A good first step is to look for places that species persisted through past climate changes (often termed ‘refugia’). We think we may have identified mini or micro-refugia for trees on deep, protected soils in the Grampians ranges, Victoria. Continue reading Where in the landscape are the refugia? →

We have a new paper out in Ecography. The aim was to link functional traits to environmental gradients. There are existing methods that do this, but they generally involve multiple steps. We created a hierarchical model that effectively joins a species distribution model with species trait values in one step. We were quite happy with the model because it worked well-better than we anticipated for rare species-and, importantly, produced sensible and interpretable results. Here is one example.

Specific leaf area (SLA) represents a tissue allocation strategy of either growing quickly or growing slowly with more tissue devoted to protection or conserving resources. SLA modifies species responses to rock cover. So, species with low SLA (thick, tough leaves) tend to increase in occurrence on increasingly rocky areas. Species with higher SLA (flimsy leaves) tend to be found on deeper, less rocky soil (see Figure).

The y-axis label looks complicated, but it’s simply the expected change in probability of species occurrence for a given change in surface rock cover. (technically, this is a partial response)..

Some key aspects of the model are:

1- species trait values actually modify species responses to environmental gradients. This may be useful for improving species distribution models when trait values are known.

2- rare species borrowed strength from common species. Species that are uncommon or have restricted distributions are usually quite difficult or impossible to model. I think this type of multi-species modelling shows real promise in this area.