Why populations can’t be saved by a single breeding pair

3 04 2018


© Reuters/Thomas Mukoya

I published this last week on The Conversation, and now reproducing it here for CB.com readers.


Two days ago, the last male northern white rhino (Ceratotherium simum cottoni) died. His passing leaves two surviving members of his subspecies: both females who are unable to bear calves.

Even though it might not be quite the end of the northern white rhino because of the possibility of implanting frozen embryos in their southern cousins (C. simum simum), in practical terms, it nevertheless represents the end of a long decline for the subspecies. It also raises the question: how many individuals does a species need to persist?

Fiction writers have enthusiastically embraced this question, most often in the post-apocalypse genre. It’s a notion with a long past; the Adam and Eve myth is of course based on a single breeding pair populating the entire world, as is the case described in the Ragnarok, the final battle of the gods in Norse mythology.

This idea dovetails neatly with the image of Noah’s animals marching “two by two” into the Ark. But the science of “minimum viable populations” tells us a different story.

No inbreeding, please

The global gold standard used to assess the extinction risk of any species is the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. Read the rest of this entry »

Predicting sustainable shark harvests when stock assessments are lacking

26 03 2018
srb 1

© Andrew Fox

I love it when a good collaboration bears fruit, and our latest paper is a good demonstration of that principle.

It all started a few years ago with an ARC Linkage Project grant we received to examine how the whaler shark fishing industry in Australia might manage its stocks better.

As I’m sure many are aware, sharks around the world aren’t doing terribly well (surprise, surprise — yet another taxon suffering at the hands of humankind). And while some populations (‘stocks’, in the dissociative parlance of the fishing industry) are doing better than others, and some countries have a better track record in managing these stocks than others, the overall outlook is grim.

One of the main reasons sharks tend to fair worse than bony fishes (teleosts) for the same fishing effort is their ‘slow’ life histories. It doesn’t take an advanced quantitative ecology degree to understand that growing slowly, breeding late, and producing few offspring is a good indication that a species can’t handle too much killing before populations start to dwindle. As is the case for most large shark species, I tend to think of them in a life-history sense as similar to large terrestrial mammals.

Now, you’d figure that a taxon with intrinsic susceptibility to fishing would have heaps of good data with which managers could monitor catches and quotas so that declines could be avoided. However, the reality is generally the inverse, with many populations having poor information regarding vital rates (e.g., survival, fertility), age structure, density feedback characteristics, and even simple estimates of abundance. Without such key information, management tends to be ad hoc and often not very effective. Read the rest of this entry »

Four decades of fragmentation

27 09 2017


I’ve recently read perhaps the most comprehensive treatise of forest fragmentation research ever compiled, and I personally view this rather readable and succinct review by Bill Laurance and colleagues as something every ecology and conservation student should read.

The ‘Biological Dynamics of Forest Fragments Project‘ (BDFFP) is unquestionably one of the most important landscape-scale experiments ever conceived and implemented, now having run 38 years since its inception in 1979. Indeed, it was way ahead of its time.

Experimental studies in ecology are comparatively rare, namely because it is difficult, expensive, and challenging in the extreme to manipulate entire ecosystems to test specific hypotheses relating to the response of biodiversity to environmental change. Thus, we ecologists tend to rely more on mensurative designs that use existing variation in the landscape (or over time) to infer mechanisms of community change. Of course, such experiments have to be large to be meaningful, which is one reason why the 1000 km2 BDFFP has been so successful as the gold standard for determining the effects of forest fragmentation on biodiversity.

And successful it has been. A quick search for ‘BDFFP’ in the Web of Knowledge database identifies > 40 peer-reviewed articles and a slew of books and book chapters arising from the project, some of which are highly cited classics in conservation ecology (e.g., doi:10.1046/j.1523-1739.2002.01025.x cited > 900 times; doi:10.1073/pnas.2336195100 cited > 200 times; doi:10.1016/j.biocon.2010.09.021 cited > 400 times; and doi:10.1111/j.1461-0248.2009.01294.x cited nearly 600 times). In fact, if we are to claim any ecological ‘laws’ at all, our understanding of fragmentation on biodiversity could be labelled as one of the few, thanks principally to the BDFFP. Read the rest of this entry »

Two new postdoctoral positions in ecological network & vegetation modelling announced

21 07 2017


With the official start of the new ARC Centre of Excellence for Australian Biodiversity and Heritage (CABAH) in July, I am pleased to announce two new CABAH-funded postdoctoral positions (a.k.a. Research Associates) in my global ecology lab at Flinders University in Adelaide (Flinders Modelling Node).

One of these positions is a little different, and represents something of an experiment. The Research Associate in Palaeo-Vegetation Modelling is being restricted to women candidates; in other words, we’re only accepting applications from women for this one. In a quest to improve the gender balance in my lab and in universities in general, this is a step in the right direction.

The project itself is not overly prescribed, but we would like something along the following lines of inquiry: Read the rest of this entry »

Sensitive numbers

22 03 2016

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 »

Outright bans of trophy hunting could do more harm than good

5 01 2016

In July 2015 an American dentist shot and killed a male lion called ‘Cecil’ with a hunting bow and arrow, an act that sparked a storm of social media outrage. Cecil was a favourite of tourists visiting Hwange National Park in Zimbabwe, and so the allegation that he was lured out of the Park to neighbouring farmland added considerable fuel to the flames of condemnation. Several other aspects of the hunt, such as baiting close to national park boundaries, were allegedly done illegally and against the spirit and ethical norms of a managed trophy hunt.

In May 2015, a Texan legally shot a critically endangered black rhino in Namibia, which also generated considerable online ire. The backlash ensued even though the male rhino was considered ‘surplus’ to Namibia’s black rhino populations, and the US$350,000 generated from the managed hunt was to be re-invested in conservation. Together, these two incidents have triggered vociferous appeals to ban trophy hunting throughout Africa.

These highly politicized events are but a small component of a large industry in Africa worth > US$215 million per year that ‘sells’ iconic animals to (mainly foreign) hunters as a means of generating otherwise scarce funds. While to most people this might seem like an abhorrent way to generate money, we argue in a new paper that sustainable-use activities, such as trophy hunting, can be an important tool in the conservationist’s toolbox. Conserving biodiversity can be expensive, so generating money is a central preoccupation of many environmental NGOs, conservation-minded individuals, government agencies and scientists. Making money for conservation in Africa is even more challenging, and so we argue that trophy hunting should and could fill some of that gap. Read the rest of this entry »

Avoiding genetic rescue not justified on genetic grounds

12 03 2015
Genetics to the rescue!

Genetics to the rescue!

I had the pleasure today of reading a new paper by one of the greatest living conservation geneticists, Dick Frankham. As some of CB readers might remember, I’ve also published some papers with Dick over the last few years, with the most recent challenging the very basis for the IUCN Red List category thresholds (i.e., in general, they’re too small).

Dick’s latest paper in Molecular Ecology is a meta-analysis designed to test whether there are any genetic grounds for NOT attempting genetic rescue for inbreeding-depressed populations. I suppose a few definitions are in order here. Genetic rescue is the process, either natural or facilitated, where inbred populations (i.e., in a conservation sense, those comprising too many individuals bonking their close relatives because the population in question is small) receive genes from another population such that their overall genetic diversity increases. In the context of conservation genetics, ‘inbreeding depression‘ simply means reduced biological fitness (fertility, survival, longevity, etc.) resulting from parents being too closely related.

Seems like an important thing to avoid, so why not attempt to facilitate gene flow among populations such that those with inbreeding depression can be ‘rescued’? In applied conservation, there are many reasons given for not attempting genetic rescue: Read the rest of this entry »