Part of the post-2020 GBF includes explicit targets for what we hope to achieve in the next decade. Devising these targets is an arduous process, where every word is chosen carefully after hours of negotiation. Using the word ‘and’ instead of ‘or’ could cost millions of dollars in government budgets. Even worse, the wrong word could potentially lead to the permanent extinction of species that have been around for millions of years.
It makes sense that at least one of the targets for the post-2020 GBF should focus on ecosystems. After all, no plant or animal can exist in isolation. Now, while I won’t be so brash to propose my own ecosystem targets, I would like to point out three pitfalls that should be avoided once government negotiators come together at the next COP meeting.
The most recent Living Planet Report was released last year, which showed that vertebrate populations around the world have declined by 68% on average. At the time, Brian McGill wrote a post at Dynamic Ecology expressing his scepticism over these large declines. His doubts stemmed from severalrelatedpapers, which all showed that vertebrate populations tended to be quite stable on average. While some populations have declined, most have remained stable and others have actually increased.
My understanding of Brian’s message (and the messages of the publishedpapers supporting him) was that while humans do affect vertebrate populations, these effects are not consistently negative. Instead, some species benefit from human actions, so human’s ultimate legacy will be rearranging relative population structure, rather than causing wholesale declines.
While I agree with this interpretation, I worry that it is causing unwarranted mistrust of the Living Planet Index. I was reminded of this last week by a passing comment by Mark Vellend:
“If biodiversity seems intractable, then just think about recent discussions of the Living Planet Index, which is based on pretty simple underlying data.”
I don’t believe the Living Planet Index exaggerates population declines. However, I also recognise that populations are stable on average. This probably seems contradictory, so I’ll use the rest of this post to explain why it isn’t.
Aspiring scientists quickly learn that where they publish their hard earned data is important for career progression. A paper in a prestigious journal pushes them up the professional ladder faster than the same paper in an obscure one.
This has led to an obsession with quantifying the prestige of a journal using impact metrics. Over at Conservation Bytes, Corey Bradshaw presents a RShiny App to rank ecology and conservation journals using a composite of different citation metrics. His app is based on a published paper, so you can be sure that it technically sound. It is not my intention to criticise these efforts; if you want to rank jounals, then this composite approach is definitely the way to go.
However, I don’t think there is much to gain from ranking journals based on their impact metrics. Moreover, scientists – especially early-career scientists – would be better off judging journals based on whether is will ensure that their paper reaches the right audience.
The world is facing a wave of populism and hyperbole, where honest discourse is less important than winning. In this post-truth world, the end justifies the means. If biodiversity conservation is a mission-driven discipline aimed at stopping the loss of species and ecosystems, should it also embrace questionable tactics?
Over the weekend, Joern Fischer wrote a criticism of transdisciplinary research. I was very eager to read it because it is something I have been wondering about over the last few months too. I began commenting on his blog, but, as my comment grew longer, I thought it is perhaps a better idea to flesh out my thoughts into a full post. Overall, I agree with Joern’s misgivings, but I would go even further to suggest that he was perhaps too forgiving towards transdisciplinary research.
If Startup for Nature encourages just one aspiring entrepreneur to launch their own conservation venture, then I’ll consider it successful. But to do this, it must first reach the right audience with the most engaging content.
Here’s the part where I ask for your help.
You can help Startup for Nature create a sub-culture of entrepreneurship amongst conservationists. Here’s how:
If you like what you see, please share it with everyone in your social network (using the sharing buttons on the website). By reaching a broader audience, we increase the chances of finding that one inspired person who might launch the next big conservation venture.
If you know of anyone who has launched their own conservation venture, or you have launched one yourself, please let me know so that I can add it to the site. Celebrating the most innovation startups will hopefully increase the uptake of entrepreneurship in conservation.
Anyone who as ever watched a David Attenborough documentary knows that biodiversity differs in areas with different climates. Only a few species an survive in hot and dry deserts whereas warm and wet tropical forests are teeming with life. But have you every stopped to wonder why this is so?
Why are certain climate conditions able to support many species and others not? More specifically, how does this work mechanistically?
Evolution is creeping into several different aspects of ecology. The latest buzz is all about integrating ecology and evolution. Perhaps you’ve heard of the latest research trends in eco-evolutionary dynamics or community phylogenetics?
Please don’t misunderstand me, I am not implying that evolution is not important in explaining patterns in nature, nor am I suggesting that we should disregard evolutionary explanations for these patterns. Instead, I believe that in order to gain a deeper understanding of ecology, we should perhaps partially blind our views using “evolution blinkers”. In fact, I’d even be so bold as to claim that unless we blind ourselves to evolution, we will never be able to fully grasp the true nature of ecological processes. Unifying ecology and evolution might actual limit our ability to build ecology as a science.
No matter at which scale you look at it, nature is remarkable.
Like many others, I was taught ecology in a very hierarchical way: individual organisms are part of a wider populations of species, collections of species form communities and communities come together to make up ecosystems. Similarly, single trees are nested within forests, which aggregate to form biomes. I’m sure you can come up with many comparable examples.
The trouble with such neat spatial hierarchies is that they lure us into believing that if patterns appear similar at several different spatial scales, then the processes leading to these patterns should also be similar. It’s so easy to assume that nature is like a set of Russian Dolls: each daughter exactly the same as its mother, only slightly smaller. But this is not necessarily the case.
One of the most fruitful sub-fields in ecology is using climate variables to predict species’ geographic distributions. For the uninitiated, species distribution modelling assumes that species are limited in their distributions to suitable climate zones. By studying the environmental conditions where species are known to occur, you can infer the total geographic distribution by calculating the suitability of unsampled regions based on the environmental. Furthermore, using the same principle, species distribution modelling can forecast the effect of future climate change of the distribution of life on earth.
The trouble lies in the spatially-structured world we live in. Species distributions, especially at large spatial scales, are spatially-autocorrelated due to constrained dispersal. Similarly, climate variables are also spatially structured because the meteorological processes at proximal regions are generally more similar than those at distant sites.
When trying to link species distributions to climate conditions, the challenge lies is separating spatial and environmental correlations in species distributions. Specifically, we should identify three patterns in the geographical species distributions.
We must first identify ‘true’ correlations with the environment, which are independent of spatial patterns (E|S).
Next, we must identify the environmental-associations that also have a strong spatial structure (E∩S). This is known as exogenous spatial autocorrelation because it is due to autocorrelation is the underlying variables.
Finally, we need to identify spatial patterns that are completely independent of environmental conditions (S|E). This is called endogenous spatial autocorrelation because it supposedly stems from spatial processes, such as dispersal.