Between the late 1990s and early 2000s, recognition of the value of scientific evidence to government decision-making grew. As interest in projecting future issues to inform policy decisions increased, we recognised that ecologists did not have the methods to conduct this type of work effectively. In the United Kingdom, the Government Office for Science established the Foresight programme to support policy making; scientific advisory committees became common, and every Ministry appointed a Chief Scientist. Given this context, we explored the use of horizon scans to assess the future and better understand uncertainties.
Imagine that you want to catalogue all of the biodiversity (all of the living organisms) from a particular location; how many trained experts would that require? How many person hours would it take to collect and identify all of the rare, well-disguised, and microscopic organisms? How many of these organisms would have to be removed from the environment and taken back to a lab for taxonomic analysis.
Although there is no substitute for human expertise, we have begun using the traces of DNA that organisms leave behind (e.g. excretions, skin and hair cells) in the environment to catalogue biodiversity. These traces of DNA, referred to as environmental DNA, can persist in the environment for minutes or can persist for centuries depending on where they end up. This field of environmental DNA (eDNA) is rapidly becoming an effective tool to complement surveys of biodiversity, both past and present.
Hello! This is my first post as Blog Editor for Methods in Ecology and Evolution and I’m thrilled to be starting with an exciting, thought-provoking topic in the wake of Halloween. But first, let me introduce myself. I currently work as a Postdoctoral Fellow and Project Manager in the Hajibabaei Lab at the Centre for Biodiversity Genomics (University of Guelph, ON, Canada) and my undergraduate and postgraduate degrees are both from Swansea University (UK). My research background is largely focused around the application of environmental DNA (i.e. free DNA found in natural environments) to detect and monitor aquatic species and answer ecological questions through both single-species detection and DNA metabarcoding.
At the moment, I’m working on the STREAM project, which combines community-based monitoring with DNA metabarcoding to gain a better understanding of freshwater health across Canada. One of my favourite parts about being in this position is the opportunity to get involved with other research being conducted in the Hajibabaei Lab. This is how I branched out into the wonderful world of bat ecology. Continue reading →
Changes in temperature and available food determine where and when animals move, reproduce, and survive. Our understanding of how environmental change impacts biodiversity and species survival is well-established at the landscape, country and global scales. But, we know less about what could happen at finer space and time scales, such as within habitats, where behavioural responses by animals are crucial for daily survival.
Simulating Movement and Daily Survival with Individual-Based Movement Models
Key questions at these scales are how the states of individuals (things like body temperature and nutritional condition) influence movement decisions in response to habitat change, and how these decisions relate to patchiness in microclimates and food. So we need tools to make reliable forecasts of how fine-scale habitat use will change under future environments. Individual-based movement simulation models are powerful tools for these kinds of studies. They let you construct habitats that vary in temperature and food conditions in both space and time and ask ‘what if’ questions. By populating these models with activity, behaviour, and movement data of animals, we can simulate different habitat conditions and predict how animals will respond to future change. Continue reading →
The source of occurrence data: fossil collections (photo by Konstantin Frisch).
To find out about changes in ancient ecosystems we need to analyse fossil databases that register the taxonomy and stratigraphic (temporal) positions of fossils. These data can be used to detect changes of taxonomic diversity and to draft time series of originations and extinctions.
The story would be so simple if it wasn’t the effects of heterogeneous and incomplete sampling: the white spots in our understanding of where and when species lived exactly. This phenomenon decreases the fidelity of face-value patterns extracted from the fossil record, making them less reliable. It must be considered if we want to get a glimpse into the biology or the distribution of life in space and time. Naturally, several metrics have been proposed to overcome this problem, each claiming to accurately depict the patterns of ancient life. Continue reading →
As human impacts on the world accelerate, so does the need for tools to monitor the effects we have on species and ecosystems. Alongside technologies like camera traps and satellite remote sensing, passive acoustic monitoring (PAM) has emerged as an increasingly valuable and flexible tool in ecology. The idea behind PAM is straightforward: autonomous acoustic sensors are placed in the field to collect audio recordings. The wildlife sounds within those recordings are then used to calculate important ecological metrics – such as species occupancy and relative abundance, behaviour and phenology, or community richness and diversity.
The Pros and Cons of Passive Acoustic Monitoring
Using sound to monitor ecosystems, rather than traditional survey methods or visual media, has many advantages. For example, it’s much easier to survey vocalising animals that are nocturnal, underwater or otherwise difficult to see. Also, because acoustic sensors capture the entire soundscape, it’s possible to calculate acoustic biodiversity metrics that aim to describe the entire vocalising animal community, as well as abiotic elements in the environment.
The use of PAM in ecology has been steadily growing for a couple of decades, mainly in bat and cetacean studies. But with sensor costs dropping and audio processing tools improving, there’s currently a massive growth in interest in applying acoustic methods to large-scale or long-term monitoring projects. As very low-cost sensors such as AudioMoth start to emerge, it’s becoming easier to deploy large numbers of sensors in the field and start collecting data. Continue reading →