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.
I meant to post this much earlier, but I just haven’t found the time. You see, I started a new job a few months ago– my first full-time academic position – and I been struggling to keep my head above water ever since.
The last year has been a whirlwind for me. Within a few months, I finished my PhD, moved back to South Africa from Belgium, spent two months of unemployment living in my childhood home before taking on the position I have now. Needless to say, I embarked on this new career path in a unprepared and frazzled state.
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?
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.
Although ecology doesn’t have many general laws, one most likely to qualify is the species-area relationship. If you walk through a field in a straight line and count all the different species you come across, you’ll notice that the total number of species increases as you progress along your straight path. After a while, however, you’ll start seeing the same species over and over again until you eventually find that you’re no longer spotting any new ones. This is the asymptotic species-area curve. While the exact mathematical form of the relationship is still hotly debated, it is safe to assume that it is an increasing function that reaches a plateau once all the species have been encountered.
As an aspiring ecologist, I am well aware that publishing a paper in Natureor Sciencewould give my career an incredible kick-start. But, like so many others, I didn’t know how to get my name printed on the glossy pages of the two oldest and most prestigious weekly scientific journals. So I did what any good scientist would do – no, this time I didn’t check Wikipedia – I knuckled down and poured over the pages in these celebrated periodicals. I spent countless nights without sleep, trying to crack the code.
Just as I was about to give up, I saw a glimmer of hope: a golden thread linking the fortunate submissions to these two behemoths of academic excellence. I managed to reverse-engineer the path to success and I will be so generous to share my astounding findings with you. But before I do that, a word of warning: my how-to guide only applies to ecological studies. Physicists, physiologists and… um… uh… anyone else (I ran out of alliterative scientific sub-fields) will have to find their own strategies. Continue reading →
There are many metaphors that use running a marathon or climbing a mountain to describe the process of ecological research. This post will not have any. No, this post will ignore linguistic devices and will shine the spotlight on behavioural psychology instead. Continue reading →