Multidisciplinary knowledge focuses on traditional academic disciplines, like ecology, law, economics, governance, ethics and sociology. It emphasises T-shaped knowledge, where a deep understanding of a narrower sub-discipline (the vertical line of the T) is married to a more general appreciation of a wider range of topics (the horizontal line of the T).
Practical skills refer to the ability to actually get things done. This includes skills like effective communication, risk assessment, conflict resolution or project management.
Finally, personal aptitudes are those – often intangible – personality traits that make people good to work with. These aptitudes include things like patience, humility, trustworthiness, leadership, punctuality, reliability etc. Continue reading →
The easiest way for an aspiring naturalist to explore wildlife is by stepping out into their own backyard. Try it yourself. As I write this, I can look through my window at a Karoo Thrush hopping about my garden. But this post is not about the tiny garden surrounding my current apartment, but rather about my childhood home. I was tremendously fortunate to grow up on a 60 hectare farm, where first discovered my love for nature.
Between wrapping up my PhD and starting my current lecturing position, I spent a few months of unemployment back in the house I grew up in. During that time, I discovered the thrill of recording unobtrusive nocturnal critters using a camera trap. Every afternoon, I set up the camera trap and eagerly explored the footage the following morning. It was so much fun.
After enough time, even I was amazed by the variety of species that lived on this relatively small farm. Perhaps, I should rephrase that last sentence. You see, calling it a farm is not accurate because it hasn’t been used for agricultural in more than ten years. The last cows were removed back in 2004 and since then the land was left dormant and only managed to minimise the risk of wild fire.
Still, I captured footage of 18 different mammal species, which is remarkably high for such a small area. Moreover, the camera trap is unlikely to capture any footage of bats and small rodents, so the total mammal diversity is probably even higher. I decided to write up these observations and the results have just been published online in the African Journal of Ecology. Continue reading →
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.
Modern conservation and environmental management rely on data. Unless you can actually show cold, hard evidence of natural deterioration, you open yourself up to criticism from denialists and other eco-skeptics. It is too easy for industry lobbyists to dismiss conservation recommendations as tree-hugger scare-mongering.
So conservationists, being the idealists that we are, decide to gather evidence for downward trends of various aspects of biodiversity. Unfortunately, efforts to quantify biodiversity trends are a major challenge. Not because measuring trends in diversity is particularly difficult, but rather because long-term monitoring is susceptible to sampling artefacts.
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.