The latest issue of Methods in Ecology and Evolution is now online! This month’s issue is a little shorter than our last few. But, as they say, good things come in small packages!
Senior Editor Lee Hsiang Liow has selected six Featured Articles this month. You can find out about all of them below. We’ve also got five Applications articles and a Practical Tools article in the April issue that we’re going to cover. Those six papers are freely available to everyone – no subscription required!
Today, science extends beyond the research bench or the fieldsite more often than ever before. Scientists are continuously interacting with educators and the general public, and people are reciprocating the interest with a drive to be involved.
With this integration of science and the public, citizen-science efforts to crowdsource information have become increasingly popular (check out Zooniverse, SciStarter, NASA Citizen Science Projects, Project FeederWatch, and Foldit to get involved!). In the birding community, enthusiasts have been observing and recording birds for decades, but now there are methods for immediate data sharing among the community (eBird).
Hackathons have become a regular feature in the data-science world. Get a group of people with a shared interest together, give them data, food, and a limited amount of time and see what they can produce (often with prizes to be won). Translated into the world of academia as research hackathons, these events are a fantastic way to foster collaboration, interdisciplinary working and skills sharing.
The Quantitative Ecology hackathon was an intense day of coding resulting in creative and innovative research ideas using social and ecological data. Teams worked through the day to develop their ideas with support from experts in R, open science and statistics. We ended up with five projects addressing questions from, ‘Who has the least access to nature?’ to ‘Where should citizen scientists go to collect new data?’.
Artificial intelligence (or AI) is an enormously hot topic, regularly hitting the news with the latest milestone where computers matching or exceeding the capacity of humans at a particular task. For ecologists, one of the most exciting and promising uses of artificial intelligence is the automatic identification of species. If this could be reliably cracked, the streams of real-time species distribution data that could be unlocked worldwide would be phenomenal.
Despite the hype and rapid improvements, we’re not quite there yet. Although AI naturalists have had some successes, they can also often make basic mistakes. But we shouldn’t be too harsh on the computers, since identifying the correct species just from a picture can be really hard. Ask an experienced naturalist and they’ll often need to know where and when the photo was taken. This information can be crucial for ruling out alternatives. There’s a reason why field guides include range maps!
Currently, most AI identification tools only use an image. So, we set out to see if a computer can be taught to think more like a human, and make use of this extra information. Continue reading →
We’ve got six papers that are freely available to absolutely everyone this month too. You can find out about two of the Open Access papers in the Applications and Practical Tools section below. In the third, Chen et al. show that tree assemblages in tropical forest ecosystems can present a strong signal of extensive distributional interspersion.
A study led by researchers at the University of Southampton has used data collected by volunteer bird watchers to study how the importance of wildlife habitat management depends on changing temperatures for British birds.
The team studied data from the British Trust for Ornithology’s Bird Atlas 2007 – 11 on the abundance of the Eurasian jay over the whole of Great Britain. The University of Southampton researchers focused on jays for this trial as they are a species of bird known to frequent a mixture of different natural environments. Continue reading →
Mathematicians and conservationists from the UK, Africa and the United States have used machine-learning and citizen science techniques to accurately count wildebeest in the Serengeti National Park in Tanzania more rapidly than is possible using traditional methods.
Evaluating wildebeest abundance is currently extremely costly and time-intensive, requiring manual counts of animals in thousands of aerial photographs of their habitats. From those counts, which can take months to complete, wildlife researchers use statistical estimates to determine the size of the population. Detecting changes in the population helps wildlife managers make more informed decisions about how best to keep herds healthy and sustainable. Continue reading →
Analysis of datasets collected on marked individuals has spurred the development of statistical methodology to account for imperfect detection. This has relevance beyond the dynamics of marked populations. A couple of great examples of this are determining site occupancy or disease infection state.
The regular series of EURING-sponsored meetings (which began in 1986) have been key to this development. They’ve brought together biological practitioners, applied modellers and theoretical statisticians to encourage an exchange of ideas, data and methods.
This new cross-journal Special Feature between Methods in Ecology and Evolution and Ecology and Evolution, edited by Rob Robinson and Beth Gardner, brings together a collection of papers from the most recent EURING meeting. That meeting was held in Barcelona, Spain, 2017, and was hosted by the Museu de Ciènces Naturals de Barcelona. Although birds have provided a convenient focus, the methods are applicable to a wide range of taxa, from plants to large mammals. 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 →