How to improve (South Australia’s) biodiversity prospects

9 04 2019
Fig2

Figure 2 (from the article). Overlaying the South Australia’s Protected Areas boundary data with the Interim Biogeographic Regionalisation for Australia layer indicates that 73.2% of the total protected area (excluding Indigenous Protected Areas) in South Australia lies in the arid biogeographic regions of Great Victoria Desert (21.1%), Channel Country (15.2%), Simpson Strzelecki Dunefields (14.0%), Nullarbor (9.8%), Stony Plains (6.6%), Gawler (6.0%), and Hampton (0.5%). The total biogeographic-region area covered by the remaining Conservation Reserves amounts to 26.2%. Background blue shading indicates relative average annual rainfall.

If you read CB.com regularly, you’ll know that late last year I blogged about the South Australia 2108 State of the Environment Report for which I was commissioned to write an ‘overview‘ of the State’s terrestrial biodiversity.

At the time I whinged that not many people seemed to take notice (something I should be used to by now in the age of extremism and not giving a tinker’s about the future health of the planet — but I digress), but it seems that quietly, quietly, at least people with some policy influence here are starting to listen.

Not satisfied with merely having my report sit on the virtual shelves at the SA Environment Protection Authority, I decided that I should probably flesh out the report and turn it into a full, peer-reviewed article.

Well, I’ve just done that, with the article now published online in Rethinking Ecology as a Perspective paper.

The paper is chock-a-block with all the same sorts of points I covered last year, but there’s a lot more, and it’s also a lot better referenced and logically sequenced.

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Biodiversity offsetting is off-putting

5 11 2018

Ancient-woodland-has-movedBiodiversity offsets are becoming more popular in Australia and elsewhere as a means to raise money for conservation and restoration while simultaneously promoting economic development (1). However, there are many perverse consequences for biodiversity if they are not set up carefully (1-3).

Biodiversity ‘offsets’ are intended to work in a similar way to carbon offsets1, in that the destruction of a part of an ecosystem (e.g., a native forest or grassland, or a wetland) can be offset by paying to fund the restoration of another, similar ecosystem elsewhere. As such, approval to clear native vegetation usually comes with financial and other conditions.

But there are several problems with biodiversity offsetting, including the inconvenient fact that creating an equivalent ecosystem somewhere takes substantially longer than it does to destroy one somewhere else (e.g., 4). While carbon emitted in one place is essentially the same as that sequestered elsewhere, a forest can take hundreds of years to develop the same biodiversity values and ecological functions it had prior to destruction. Read the rest of this entry »





Offshore Energy & Marine Spatial Planning

22 02 2018

FishingOffshoreWind

I have the pleasure (and relief) of announcing a new book that’s nearly ready to buy, and I think many readers of CB.com might be interested in what it describes. I know it might be a bit premature to announce it, but given that we’ve just finished the last few details (e.g., and index) and the book is ready to pre-order online, I don’t think it’s too precocious to advertise now.

9781138954533-2

A little history is in order. The brilliant and hard-working Katherine Yates (now at the University of Salford in Manchester, UK) approached me back in 2014 to assist her with co-editing the volume that she wanted to propose for the Routledge Earthscan Ocean series. I admit that I reluctantly agreed at the time, knowing full well what was in store (anyone who has already edited a book will know what I mean). Being an active researcher in energy and biodiversity (perhaps not so much on the ‘planning’ side per se) certainly helped in my decision.

And yes, there were ups and downs, and sometimes it was a helluva lot of work, but Katherine certainly made my life easier, and she has finally driven the whole thing to completion. She deserves most of the credit.

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Bring it back

13 02 2018
fynbos

Protea compacta in fynbos, a form of shrubland at Soetanysberg, South Africa. Photo: Brian van Wilgen

Restoration of lost habitats and ecosystems hits all the right notes — conservation optimism, a can-do attitude, and the excitement of seeing biologically impoverished areas teem with life once more.

The Strategic Plan of the Convention on Biological Diversity includes a target to restore at least 15% of degraded ecosystems. This is being enthusiastically taken up in many places, including through initiatives such as the Bonn Challenge, a global aspiration to restore 350 million hectares of deforested and degraded land by 2030. This is in recognition of the importance of healthy ecosystems in not just conserving biodiversity, but also in combating climate change. Peatlands and forests lock away carbon, while grassland diversity stabilises ecosystem productivity during extreme weather events. So how can we make sure that these restoration efforts are as effective as possible? Read the rest of this entry »






Massive yet grossly underestimated global costs of invasive insects

4 10 2016
Portrait of a red imported fire ant, Solenopsis invicta. This species arrived to the southeastern United States from South America in the 1930s. Specimen from Brackenridge Field Laboratory, Austin, Texas, USA. Public domain image by Alex Wild, produced by the University of Texas "Insects Unlocked" program.

Portrait of a red imported fire ant Solenopsis invicta. This species arrived to the southeastern USA from South America in the 1930s. Specimen from Brackenridge Field Laboratory, Austin, Texas, USA. Public domain image by Alex Wild, produced by the University of Texas “Insects Unlocked” program.

As many of you already know, I spent a good deal of time in France last year basking in the hospitality of Franck Courchamp and his vibrant Systematic Ecology & Evolution lab at Université Paris-Sud. Of course, I had a wonderful time and was sad to leave in the end, but now I have some hard evidence that I wasn’t just eating cheese and visiting castles. I was actually doing some pretty cool science too.

Financed by BNP-Paribas and Agence Nationale de Recherche, the project InvaCost was designed to look at the global impact of invasive insects, including projections of range dynamics under climate change and shifting trade patterns. The first of hopefully many papers is now out.

Just published in Nature Communications, I am proud that many months of hard work by a brilliant team of ecologists, epidemiologists and economists has culminated in this article entitled Massive yet grossly underestimated costs of invasive insects, which in my opinion is  the first robust analysis of its kind. Despite some previous attempts at estimating the global costs of invasive species1-4 (which have been largely exposed as guesswork and fantasy5-10), our paper rigorously treats the economic cost estimates and categorises them into ‘reproducible’ and ‘irreproducible’ categories.

Lymantria

Gypsy moth (Lymantria dispar) adult. Dimitri Geystor (France)

What we found was sobering. If we look at just ‘goods and services’ affected by invasive insects, the annual global costs run at about US$70 billion. These include agricultural, forestry and infrastructure damages, as well as many of the direct costs of clean-up and eradication, and the indirect costs of prevention. When you examine that number a little more closely and only include the ‘reproducible’ studies, the total annual costs dip to about US$25 billion, meaning that almost 65% of the costs recorded are without any real empirical support. Scary, especially considering how much credence people put on previously published global ‘estimates’ (for example, see some citation statistics here).

Coptotermes_formosanus

Formosan subterranean termite Coptotermes formosanus by Scott Bauer, US Department of Agriculture, Agricultural Research Service

There’s a great example to illustrate this. If you take it at face value, the most expensive invasive insect in the world is the Formosan subterranean termite Coptotermes formosanus estimated at US$30.2 billion/yr globally. However, that irreproducible estimate is based on a single non-sourced value of US$2.2 billion per year for the USA, a personal communication supporting a ratio of 1:4 of control:repair costs in a single US city (New Orleans), and an unvalidated assumption that the US costs represent 50% of the global total.

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Sensitive numbers

22 03 2016
toondoo.com

A sensitive parameter

You couldn’t really do ecology if you didn’t know how to construct even the most basic mathematical model — even a simple regression is a model (the non-random relationship of some variable to another). The good thing about even these simple models is that it is fairly straightforward to interpret the ‘strength’ of the relationship, in other words, how much variation in one thing can be explained by variation in another. Provided the relationship is real (not random), and provided there is at least some indirect causation implied (i.e., it is not just a spurious coincidence), then there are many simple statistics that quantify this strength — in the case of our simple regression, the coefficient of determination (R2) statistic is a usually a good approximation of this.

In the case of more complex multivariate correlation models, then sometimes the coefficient of determination is insufficient, in which case you might need to rely on statistics such as the proportion of deviance explained, or the marginal and/or conditional variance explained.

When you go beyond this correlative model approach and start constructing more mechanistic models that emulate ecological phenomena from the bottom-up, things get a little more complicated when it comes to quantifying the strength of relationships. Perhaps the most well-known category of such mechanistic models is the humble population viability analysis, abbreviated to PVA§.

Let’s take the simple case of a four-parameter population model we could use to project population size over the next 10 years for an endangered species that we’re introducing to a new habitat. We’ll assume that we have the following information: the size of the founding (introduced) population (n), the juvenile survival rate (Sj, proportion juveniles surviving from birth to the first year), the adult survival rate (Sa, the annual rate of surviving adults to year 1 to maximum longevity), and the fertility rate of mature females (m, number of offspring born per female per reproductive cycle). Each one of these parameters has an associated uncertainty (ε) that combines both measurement error and environmental variation.

If we just took the mean value of each of these three demographic rates (survivals and fertility) and project a founding population of = 10 individuals for 1o years into the future, we would have a single, deterministic estimate of the average outcome of introducing 10 individuals. As we already know, however, the variability, or stochasticity, is more important than the average outcome, because uncertainty in the parameter values (ε) will mean that a non-negligible number of model iterations will result in the extinction of the introduced population. This is something that most conservationists will obviously want to minimise.

So each time we run an iteration of the model, and generally for each breeding interval (most often 1 year at a time), we choose (based on some random-sampling regime) a different value for each parameter. This will give us a distribution of outcomes after the 10-year projection. Let’s say we did 1000 iterations like this; taking the number of times that the population went extinct over these iterations would provide us with an estimate of the population’s extinction probability over that interval. Of course, we would probably also vary the size of the founding population (say, between 10 and 100), to see at what point the extinction probability became acceptably low for managers (i.e., as close to zero as possible), but not unacceptably high that it would be too laborious or expensive to introduce that many individuals. Read the rest of this entry »