The Full Picture

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Predicting the impact of climate change

on biodiversity – a GEOSS scenario

Stefano Nativi and Paolo Mazzetti, Italian National Research Council;

Hannu Saarenmaa, Finnish Museum of Natural History;

Jeremy Kerr and Heather Kharouba, Canadian Facility for Ecoinformatics Research;

Éamonn Ó Tuama, Global Biodiversity Information Facility;

Siri Jodha Singh Khalsa, National Snow and Ice Data Center

hile some two million plus species have been

described, and many millions more remain to be

discovered, climate change threatens to commit 15

to 37 per cent of these to extinction by 2050, accelerating a

dangerous trend that land use change has already set in

motion. An extinction episode of this magnitude would likely

severely degrade the quality of vital ecosystem services, such

as nutrient cycling, atmospheric regulation, soil formation,

water purification, and pollination, upon which the human

enterprise relies. Scientists are presented with the formidable

challenge of assessing likely impacts of unprecedented inter-

actions between rapid climate and land use changes,

predicting how those impacts will unfold into the future, and

providing policy options to decision-makers. These issues have

been highlighted in stark terms in the newly released Fourth

Assessment Report of the Intergovernmental Panel on Climate

Change.

In short, global change requires a monumental scientific response,

drawing on infrastructure that integrates the enormous volumes

of data available from biodiversity research, earth observations,

and climate models. Components of this megascience infrastruc-

ture already exist, having been established by the IPCC and Global

Biodiversity Information Facility (GBIF). Integrating these

disparate components will require great effort in terms of meta-

data development and related service coordination. However, the

Global Earth Observation System of Systems (GEOSS) provides

the basis for realizing these goals through its interoperability infra-

structure.

Here, we describe the results of linking the biodiversity and

climate change research infrastructures to enable scientists to

conduct new, broad-scale ecological analyses. We describe a

generic use scenario and a related modelling workbench for study-

ing the impacts of climate change on biodiversity. A scenario, as

described here, provides a basis for predicting biodiversity impacts

of climate change into the future by demonstrating recent impacts

of anthropogenic changes in the 20th century. Models such as this

are built using the infrastructure being developed by GEOSS and

provide an essential benchmark against which forecasts for the

future might be constructed. This development has been

conducted in the framework of the GEOSS

Interoperability Process Pilot Project initiative.

Scenario definition

One of the most widely used techniques for large-scale

biodiversity data analysis is Ecological Niche

Modelling (ENM), which was pioneered by Peterson

et al.

2,3

and refined subsequently by many others.

ENM is now employed in a range of global change and

macroecological applications.

5,6

GBIF has promoted

this approach and organised several international

workshops on the topic.

A scenario for predicting the impact of climate

change on biodiversity involves several steps

Step one

– Identify the species for which sufficient

data exist: such data should span at least 30 years.

There are many biodiversity datasets that satisfy these

stringent criteria and, although they are usually

patchily distributed (for example, birds from the

United Kingdom, butterflies from Canada, and so on),

ENM can be applied to them. However, identifying the

existence of the datasets is a challenge. If multiple

datasets are cached in a repository somewhere, cluster

analysis and data mining can be used to discover the

most suitable datasets. If caching or other central

repositories do not exist, expert human advice is

needed to select the datasets.

Step two

– Assemble biodiversity datasets and map

their spatial and temporal distributions, after which

gaps in data become clear. Such gaps can provide new

data sharing opportunities within and among coun-

tries, and the need for more and better data can be

communicated to policy-makers. Presentation of such

spatial trends can also encourage additional data

providers to permit access to their data holdings.

Step three

– Determine which environmental char-

acteristics are most likely to influence target species’

niches. High resolution land cover and climate data

are commonly required for this purpose. Although

satellite data have not yet been widely or effectively

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