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Published: 17 October 2011

‘Top down’ approach to biodiversity under climate change

Karel Mokany

In a recent ECOS article, Tim Low highlighted the importance of making robust predictions of climate change impacts on biodiversity. Formulating conservation and management strategies that best retain biodiversity into the future depend on such reliable predictions.1 We need to answer key questions such as ‘Where are the best places across the country to put new conservation reserves?’, and ‘Which areas should be the focus of restoration efforts?’

New Zealand has a high diversity of unique land snails – more than 1000 species, ranging from giant carnivorous species to the very small (above). At some forest sites, communities of 30 to 70 snail species occur within a few square metres; this contrasts markedly with the 5–10 species typical of land snail communities elsewhere. Macroecological modelling can predict the total number of species expected to occur within any defined area.
Credit: David Winter/The Atavism

Ironically, one of the greatest impediments to predicting climate change impacts on biodiversity is the sheer number of species that we need to conserve. A recent estimate suggests that Australia has more than half a million native species.2

Through state and federal legislation, Australians have assumed the responsibility of custodian for these species. This means providing all of them with the opportunity to persist under a changing climate. To identify robust conservation and management strategies, predictions of climate change impacts need to be relevant to biodiversity as a whole – that is, to many species from many different taxonomic groups.3

Until now, most related ecological research has focused on predicting changes in the distribution of individual species in response to climate change.4 However, due to the substantial information collection and modelling effort required for each species, predictions for all species in a taxonomic group are only possible for a small number of well-studied taxa (e.g. birds, mammals, reptiles).5,6

In his earlier ECOS article, Tim Low also pointed out a number of practical challenges in accurately predicting the potential distribution of individual species.

For highly diverse, yet poorly studied taxa – such as plants, insects and marine invertebrates – alternative approaches are needed to predict climate change impacts relevant to all species. Enter ‘macroecology’, a scientific discipline that can predict changes in broad patterns of biodiversity over time.

Macroecological modelling is particularly valuable for highly diverse, poorly studied taxa, as it provides information relevant to all species: even those we know little about.

Two powerful macroecological modelling approaches involve predicting either changes in the number of species in each location, or dissimilarity in community composition between pairs of locations.7 Both approaches have demonstrated their value in predicting climate change impacts on diverse species within multiple taxonomic groups over large regions.8,9

CSIRO’s Macroecological Modelling team is now developing techniques for extending the capacity of macroecology to predict climate change impacts on Australia’s biodiversity.

This map based on CSIRO’s macroecological modelling approach shows the total number of land snail species expected to occur within a 3.2 km radius of different locations in New Zealand; more diverse areas are shown in green and less diverse areas in brown.
Credit: CSIRO

One new approach10 combines models of species richness and compositional dissimilarity to predict the occurrences of all species across all locations over large regions. The same approach can directly predict the impacts of climate change on biodiversity, or form the basis for more mechanistic macroecological predictions of biodiversity change.

Macroecological modelling has significant untapped potential to complement the substantial research effort focused on predicting changes in the distributions of individual species under climate change. By providing predictions relevant to all species, it can help to formulate conservation and management strategies that best retain biodiversity under climate change.

Karel Mokany is an ecologist with CSIRO Ecosystem Sciences, developing new macroecological approaches to predicting climate change impacts on biodiversity.


1 Botkin DB et al. (2007). Forecasting the effects of global warming on biodiversity. Bioscience 57, 227–36.
2 Chapman A (2009). Numbers of Living Species in Australia and the World. Department of the Environment, Water, Heritage and the Arts, Canberra, p. 84.
3 Margules CR and Pressey RL (2000). Systematic conservation planning. Nature 405, 243–53.
4 Elith J and Leathwick JR (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40, 677–97.
5 Thuiller W et al. (2006). Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions. Global Change Biology 12, 424–40.
6 Hole DG et al. (2009). Projected impacts of climate change on a continent-wide protected area network. Ecology Letters 12, 420–31.
7 Mokany K and Ferrier S (2011). Predicting impacts of climate change on biodiversity: a role for semi-mechanistic community-level modelling. Diversity and Distributions 17, 374–80.
8 Ferrier S et al. (2010). Using generalised dissimilarity modelling to assess potential impacts of climate change on biodiversity composition in Australia, and on the representativeness of the national reserve system. Report for Department of Sustainability, Environment, Water, Population and Communities, Canberra.
9 Sommer JH et al. (2010). Projected impacts of climate change on regional capacities for global plant species richness. Proceedings of the Royal Society B 277, 2271–80.
10 Mokany K et al. (2011). Combining α- and β-diversity models to fill gaps in our knowledge of biodiversity. Ecology Letters 14, 1043–51.





Published: 3 June 2013

Regional marine forecasting on horizon for Indian Ocean Rim


Indian Ocean nations met in Perth last week to discuss opportunities to develop regional ocean forecasting. As well as its importance to Western Australia, the Indian Ocean region is influential for the climate and rainfall of the south-western and southern parts of the continent.

The BLUElink program, a collaboration between scientists and the Australian Navy, is central to this regional initiative. Here, HMAS <i>Ararat (II) </i>renders assistance to the <i>Lady Amber</i>, a 35-foot schooner that has been conducting oceanographic research in the Indian Ocean. Deployed in 2011, the schooner has criss-crossed the Southern Indian Ocean launching dozens of Argo drifting robots to gather data about the health of the ocean.
The BLUElink program, a collaboration between scientists and the Australian Navy, is central to this regional initiative. Here, HMAS Ararat (II) renders assistance to the Lady Amber, a 35-foot schooner that has been conducting oceanographic research in the Indian Ocean. Deployed in 2011, the schooner has criss-crossed the Southern Indian Ocean launching dozens of Argo drifting robots to gather data about the health of the ocean.
Credit: Royal Australian Navy

Almost all member countries of the Indian Ocean Rim Association for Regional Cooperation (IOR-ARC) attended the week-long workshop, designed to advance cooperation and understanding on international ocean forecasting capabilities and needs in the Indian Ocean.

Australia’s ocean forecasting system, BLUElink which is used to predict sub-surface ocean conditions for environmental and industrial applications, was a guide for the meeting.

BLUElink utilises the full suite of ocean (Argo, drifting, moored) and satellite (sea surface height) observations, and models these to simulate ocean conditions for up to seven days. It generates forecasts for marine industries (fishing, shipping, oil and gas); for search and rescue; for environmental protection in the case of oil spills, and for environmental management of fish stocks.

IOR-ARC convenor, Dr Andreas Schiller from CSIRO’s Wealth from Oceans Flagship, said Australia has a long record of working with Indian Ocean Rim countries on marine, climate and oceanographic issues and the workshop will continue that tradition.

‘Access to ocean observing and forecasting systems and the ability to visualise and interpret this information will assist Indian Ocean Rim nations in improving preparedness for and dealing with marine disasters, search and rescue, and emergency response activities.

‘For governments and non-government organisations, there are considerable advantages to using environmental information from ocean forecasting systems to improve the livelihood of local fishermen and for marine industries promoting the sustainability of catch rates through environmental information.

‘Ocean observations and ocean forecasting provide the basis on which many of the climate-related coastal features and extremes such as coastal storm surge and tropical cyclone predictions can be assessed and monitored,’ Dr Schiller said.

CSIRO and Bureau of Meteorology scientists are currently developing the next generation global ocean forecasting models that will predict near-global ocean conditions up to seven days ahead. The models will be of benefit for defence, environmental protection and biodiversity conservation, shipping and recreational marine applications.

The researchers have been working with the Royal Australian Navy to develop the forecasting capability, as part of BLUElink. BLUElink is now operational and forecasts are available to the public. Australia has extended that capability and will soon have a capacity to forecast conditions for any of the world’s oceans.

Dr Schiller said a complementary capacity-building program began five years ago under the Indian Ocean Global Ocean Observing System (IOGOOS) Regional Alliance facilitated by the UNESCO Intergovernmental Oceanographic Commission’s support office in Perth. Dr Schiller is the current Chair of IOGOOS.

Just as for atmospheric weather forecasts, ocean prediction also requires a comprehensive and freely shared ocean observation network.

Although some of the related tools and models are still under development, during the implementation period of a full program it is likely that these tools will be readily available and applicable to the Indian Ocean Rim Association for Regional Cooperation.

Dr Schiller said South African and Indian scientists have already begun ocean research programs to build their own forecasting capabilities.

Source: CSIRO






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