The study of interactions and their impacts on communities is a fundamental part of ecology. Much work has been done on measuring the interactions between species and their impacts on relative abundances of species. Progress has been made in understanding of the interactions at the ecological level, but we know that co-evolution is important in shaping the structure of communities in terms of the species that live there and their characteristics. Continue reading →
Plant-pollinator interactions are often considered to be the textbook example of co-evolution. But specialised interactions between plants and pollinators are the exception, not the rule. Plants tend to be visited by many different putative pollinator species, and pollinating insects tend to visit many plant hosts. This means that diffuse co-evolution is a much more likely driver of speciation in these communities. So, the standard phylogenetic methods for evaluating co-evolution aren’t applicable in most plant-pollinator interactions. Also, many plant-pollinator communities involve insect species for which we do not yet have fully resolved phylogenies. Continue reading →
The Struggle is Real: Finding Interesting and Relevant Articles
Where to start? We are awash in data, information, papers, and books. There are hundreds of ecological and environmental journals published regularly around the world; the British Ecological Society alone publishes five journals and is now accepting submissions for a sixth (more information on People and Nature here).
None of us has time even to click on the various articles flagged by alerts, feeds, or keywords, and few even browse tables of contents (which are becoming irrelevant as we move to DOIs and immediate-online publication). Increasingly, we rely on our friends, colleagues, students, and mentors to point us towards papers we might find interesting – further evidence, I suppose, of the importance of good networks for knowledge creation and scientific understanding.
Regular readers of Methods in Ecology and Evolution or this Methods blog may not realise how many methodological papers are published routinely in our BES sister journals. In this inaugural posting of Also of interest…, I highlight three papers recently published in Journal of Applied Ecology that introduce and apply new, model-based methodology to interesting ecological questions. The specific methods are like many seen in the pages of Methods in Ecology and Evolution and suggest general approaches for modelling and studying complex ecological and environmental phenomena. Continue reading →
Understanding how wild populations respond and adapt to environmental change is a key question in evolutionary biology. To understand this, we need to be able to separate genetic and environmental effects on phenotypic variation. Statistical ‘animal models’, which can do just this, have revolutionised the field of quantitative genetics. A lack of full knowledge of individual pedigrees can lead to severe bias in quantitative genetic parameter estimates though – particularly when genetic values for focal traits vary non-randomly in unknown parents.
Datasets used by quantitative ecologists are getting more and more complex. So we need more complex models, such as hierarchical and complex spatial models. Typically, Bayesian approaches such as Markov chain Monte Carlo have been used. But these methods can be slow, making it infeasible to fit some models.
The use of molecular methods for monitoring and surveillance of organisms in aquatic and marine systems has become more and more common. We’ve since expanded this technology this through using both captured whole organisms and collecting/filtering environmental DNA (eDNA). These methods naturally migrated from single species, active surveillance methods towards using high throughput sequencing as a method of passive surveillance via metabarcoding.
I’d recommend this paper to all researchers and management groups interested in applying metabarcoding techniques to answer both experimental and applied questions. The design of this article will provide both experienced researchers and those new to the field with important information to further this rapidly expanding field.
As a quantitative ecologist, I sometimes attempt to model species’ abundance and distribution changes in response to environmental change. Often these are species that, for one reason or another, we know a lot about. They may be high profile species of conservation concern, or have some economic or cultural importance. Some are simply model species that many people have studied because they’re easy to study because many people have studied them. Just as often though, we’re missing crucial data on one or more parameters. Frustratingly we don’t always have the time or resources to collect the new ecological or biological data required. Continue reading →
Correlative distribution models have become essential tools in conservation, macroecology and ecology more generally. They help turn limited occurrence records into predictive maps that help us get a better sense of where species might be found, which areas might be critical for their protection, how large their range currently is, and how it might change with climate change, urban encroachment or other forms of habitat conversion.
It can be frustrating, however, when species distribution models (and the predictive maps they produce) don’t adequately capture what we already know about the habitat needs of a species. A major challenge to date has been to represent the environmental needs of species that require distinct habitats during different life stages or behavioural states. Rainbow parrotfish (Scarus guacamaia), for example, spend their youth sheltered from predators in mangrove areas before moving onto coral reefs, and European nightjars (Caprimulgus europaeus) breed in heathland but require access to grazed grassland for foraging. Correlative distribution models confronted with occurrence records from both life stages or behavioural modes tend to produce poor predictive maps because they confound these distinct requirements. Continue reading →
The rise of trait ecology led to many quantitative frameworks to understand the underlying rules that determine how species are assembled into local communities from regional pools. Ecologists are interested in understanding whether environmental features select for particular traits that optimise local fitness and regulate species co-existence.
In ‘Assessing the joint behaviour of species traits as filtered by environment’, Erin Schliep and her co-authors aimed to develop a joint probabilistic model under a Bayesian framework to help explain the correlations among traits and how trait distributions differ across species and their environments. The end product is a model of trait-environmental relationships that takes full advantage of information on intra- and interspecific variation typically found within and among species. Continue reading →
Understanding key habitat requirements is critical to the conservation of species at risk. For highly mobile species, discerning what is key habitat as opposed to areas that are simply being traversed (perhaps in the search for key habitats) can be challenging. For seabirds, in particular, it can be difficult to know which areas in the sea represent key foraging grounds. Devices that record birds’ diving behaviour can help shed light on this, but they’re expensive to deploy. In contrast, devices that record the birds’ geographic position are more commonly available and have been around for some time.
In their recent study entitled ‘Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds,’ Ella Browning and her colleagues made use of a rich dataset on 399 individual birds from three species, some equipped with both global positioning (GPS) and depth recorder devices, others with GPS only. The data allowed them to test whether deep learning methods can identify when the birds are diving (foraging) based on GPS data alone. Results were highly promising, with top models able to distinguish non-diving and diving behaviours with 94% and 80% accuracy. Continue reading →