Cartoon guide to biodiversity loss XXXII

8 09 2015

Six more biodiversity cartoons — this time, from France. They’re in French to pay hommage to my hosts (and acknowledge their fanaticism for les bandes dessinées), but don’t worry, I’ve provided full translation (see full stock of previous ‘Cartoon guide to biodiversity loss’ compendia here).

“Biodiversity: More and more species threatened. The good news for you is that you’re not endangered. The bad news is that neither are we.” © Roulies

Read the rest of this entry »

Australia’s motto: “Screw the environment!”

2 09 2015
Mmmmm! I love coal!

Mmmmm! I love coal!

An article originally posted on ALERT by April Reside (with permission to reproduce).

The Conservative Tony Abbott government in Australia is proposing alarming changes to Australia’s Environmental Protection and Biodiversity Conservation (EPBC) Act 1999 — a remarkable move that would prevent environment groups from challenging many damaging development projects.

This has all come to a head over the Carmichael Coal Mine — a plan to build a massive mine in central Queensland in order to export 60 million of tonnes of coal to India each year.

Coal, of course, is the dirtiest of all fossil fuels, and India’s plan to burn it by the shipload for electricity is bad news for the planet.

The Abbott government is in a tizzy after after a community organization, the Mackay Conservation Group, challenged the approval of the Carmichael Mine in Australia’s Federal Court.

The community group says Environment Minister Greg Hunt didn’t properly consider the impact the mine would have on two threatened species, the yakka skink and ornamental snake.

The mine site also sustains the largest population of the southern subspecies of the black-throated Finch, which is endangered.

The implications of the mega-mine go well beyond a few imperilled species. If the mine goes ahead, it will be one of the biggest in the world — and the emissions from burning its mountains of coal would cancel out all gains made from Australia’s current emissions-reduction strategy.

On top of the frightening precedent it would set, the Abbott government appears to be double-dealing. Read the rest of this entry »

Scariest part of climate change isn’t what we know, but what we don’t

7 08 2015

© Nick Kim

My good friend and tropical conservation rockstar, Bill Laurancejust emailed me and asked if I could repost his recent The Conversation article here on

He said:

It’s going completely viral (26,000 reads so far) in just three days. It’s been republished in The Ecologist, I Fucking Love Science, and several other big media outlets.

Several non-scientists have said it really helped them to understand what’s known versus unknown in climate-change research—which was helpful because they feel pummelled by all the research and draconian stuff that gets reported and have problems parsing out what’s likely versus speculative.

With an introduction like that, you’ll just have to read it!

“It’s tough to make predictions, especially about the future”: so goes a Danish proverb attributed variously to baseball coach Yogi Berra and physicist Niels Bohr. Yet some things are so important — such as projecting the future impacts of climate change on the environment — that we obviously must try.

An Australian study published last week predicts that some rainforest plants could see their ranges reduced 95% by 2080. How can we make sense of that given the plethora of climate predictions?

In a 2002 press briefing, Donald Rumsfeld, President George W. Bush’s Secretary of Defence, distinguished among different kinds of uncertainty: things we know, things we know we don’t know, and things we don’t know we don’t know. Though derided at the time for playing word games, Rumsfeld was actually making a good point: it’s vital to be clear about what we’re unclear about.

So here’s my attempt to summarise what we think we know, don’t know, and things that could surprise us about climate change and the environment.

Things we think we know

We know that carbon dioxide levels in the atmosphere have risen markedly in the last two centuries, especially in recent decades, and the Earth is getting warmer. Furthermore, 2014 was the hottest year ever recorded. That’s consistent with what we’d expect from the greenhouse effect. Read the rest of this entry »

All (fisheries) models are wrong, but some are useful (to indigenous people)

1 08 2015

miracle_cartoonAnother post from Alejandro Frid. (Note: title modified from George Box‘s most excellent quote).

As an ecologist working for indigenous people of coastal British Columbia, western Canada, I live at the interface of two worlds. On the one hand, I know that computer models can be important management tools. On the other hand, my job constantly reminds me that whether a model actually improves fishery management depends, fundamentally, on the worldview that shapes the model’s objectives. To explore why, I will first review some general concepts about what models can and cannot do. After that, I will summarize a recent model of herring populations and then pull it all together in a way that matters to indigenous people who rely on marine resources for cultural integrity and food security.

Models do a great job of distilling the essence of how an ecosystem might respond to external forces—such as fisheries—but only under the specific conditions that the modeller assumes to be true in the ‘world’ of the model. Sometimes these assumptions are well-grounded in reality. Sometimes they are blatant but necessary simplifications. Otherwise, it would be difficult to ask questions about how major forces for which we have no historical precedent—such as the combined effects of industrial fisheries, ocean acidification and climate change—might be altering the ocean. For instance, due to our greenhouse gas emissions, the ocean is warming and contains less dissolved oxygen. These stressful conditions hamper the capacity of fish to grow, and appear to be on their way to shrinking the body sizes of entire fish communities1. If you want even to begin to comprehend what the ocean will look like in the long term due to these effects of climate change, it makes sense to assume, in the ‘world’ of your model, that fishing does not exist, even though you know it does. Of course, you would then acknowledge that climate change probably exacerbates the effects of fisheries, which highlights that you still have to examine the combination of these effects. And that is exactly what an excellent team of modellers did1. Read the rest of this entry »

Ice Age? No. Abrupt warmings and hunting together polished off Holarctic megafauna

24 07 2015
Oh shit oh shit oh shit ...

Oh shit oh shit oh shit …

Did ice ages cause the Pleistocene megafauna to go extinct? Contrary to popular opinion, no, they didn’t. But climate change did have something to do with them, only it was global warming events instead.

Just out today in Science, our long-time-coming (9 years in total if you count the time from the original idea to today) paper ‘Abrupt warmings drove Late Pleistocene Holarctic megafaunal turnover‘ led by Alan Cooper of the Australian Centre for Ancient DNA and Chris Turney of the UNSW Climate Change Research Centre demonstrates for the first time that abrupt warming periods over the last 60,000 years were at least partially responsible for the collapse of the megafauna in Eurasia and North America.

You might recall that I’ve been a bit sceptical of claims that climate changes had much to do with megafauna extinctions during the Late Pleistocene and early Holocene, mainly because of the overwhelming evidence that humans had a big part to play in their demise (surprise, surprise). What I’ve rejected though isn’t so much that climate had nothing to do with the extinctions; rather, I took issue with claims that climate change was the dominant driver. I’ve also had problems with blanket claims that it was ‘always this’ or ‘always that’, when the complexity of biogeography and community dynamics means that it was most assuredly more complicated than most people think.

I’m happy to say that our latest paper indeed demonstrates the complexity of megafauna extinctions, and that it took a heap of fairly complex datasets and analyses to demonstrate. Not only were the data varied – the combination of scientists involved was just as eclectic, with ancient DNA specialists, palaeo-climatologists and ecological modellers (including yours truly) assembled to make sense of the complicated story that the data ultimately revealed. Read the rest of this entry »

Cartoon guide to biodiversity loss XXX 30

27 05 2015

[10.06.2015 update: Because of all the people looking for cartoon porn, I’ve slightly altered the title of this post. Should have predicted that one]

Third batch of six biodiversity cartoons for 2015 (see full stock of previous ‘Cartoon guide to biodiversity loss’ compendia here).

Read the rest of this entry »

Statistical explainer: average temperature increases can be deceiving

12 05 2015

Beating-the-Heat-Without-PowerOver the years I’ve used a simple graphic from the IPCC 2007 Report to explain to people without a strong background in statistics just why average temperature increases can be deceiving. If you’re not well-versed in probability theory (i.e., most people), it’s perhaps understandable why so few of us appear to be up-in-arms about climate change. I posit that if people had a better appreciation of mathematics, there would be far less inertia in dealing with the problem.

Instead of using the same image, I’ve done up a few basic graphs that explain the concept of why average increases in temperature can be deceiving; in other words, I explain why focussing on the ‘average’ projected increases will not enable you to appreciate the most dangerous aspects of a disrupted climate – the frequency of extreme events. Please forgive me if you find this little explainer too basic – if you have a modicum of probability theory tucked away in your educational past, then this will be of little insight. However, you may wish to use these graphs to explain the problem to others who are less up-to-speed than you.

Let’s take, for example, all the maximum daily temperature data from a single location compiled over the last 100 years. We’ll assume for the moment that there has been no upward trend in the data over this time. If you plot the frequency of these temperatures in, say, 2-degree bins over those 100 years, you might get something like this:


This is simply an illustration, but here the long-term annual average temperature is 25 degrees Celsius, and the standard deviation is 5 degrees. In other words, over those 100 years, the average daily maximum temperature is 25 degrees, but there were a few days when the maximum was < 10 degrees, and a few others where it was > 40 degrees. This could represent a lot of different places in the world.

We can now fit what’s known as a ‘probability density function’ to this histogram to obtain a curve of expected probability of any temperature within that range:


If you’ve got some background in statistics, then you’ll know that this is simply a normal (Gaussian) distribution. With this density function, we can now calculate the probability of any particular day’s maximum temperature being above or below any particular threshold we choose. In the case of the mean (25 degrees), we know that exactly half (p = 0.50) of the days will have a maximum temperature below it, and exactly half above it. In other words, this is simply the area under the density function itself (the total area under the entire curve = 1). Read the rest of this entry »


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