‘Just Google it’ marks an important step in converting ecology to an armchair science. Many species (e.g. owls, hawks, bears) are difficult, time-consuming, expensive and even dangerous to observe. It would be a lot easier if we didn’t have to spend time, energy and risk lives having to observe organisms in the field! Continue reading →
Francesco de Bello describes the main elements of the method he has recently published in Methods in Ecology and Evolution. The method aims at decoupling and combining functional trait and phylogenetic dissimilarities between organisms. This allows for a more effective combination of non-overlapping information between phylogeny and functional traits. Decoupling trait and phylogenetic information can also uncover otherwise hidden signals underlying species coexistence and turnover, by revealing the importance of functional differentiation between phylogenetically related species.
In the video Francesco visually represents what the authors think their tool is doing with the data so you can see its potential. This method can provide an avenue for connecting macro-evolutionary and local factors affecting coexistence and for understanding how complex species differences affect multiple ecosystem functions.
BACIPS (Before-After Control-Impact Paired Series) is probably the best-known and most powerful approach to detect and quantify human interventions on ecosystems. In BACIPS designs, Impact and Control sites are sampled simultaneously (or nearly so) multiple times Before and After an intervention. For each sampling survey conducted Before or After, the difference in the sampled response variable (e.g. density) is calculated. Before and After differences are then compared to provide a measure of the effect of the intervention, assuming that the magnitude of the induced change is constant through time. However, many interventions may not cause immediate, constant changes to a system.
We developed a new statistical approach – called Progressive-Change BACIPS (Before-After Control-Impact Paired-Series) – that extends and generalises the scope of BACIPS analyses to time-dependent effects. After quantifying the statistical power and accuracy of the method with simulated data sets, we used marine and terrestrial case studies to illustrate and validate their approach. We found that the Progressive-Change BACIPS works pretty well to estimate the effects of environmental impacts and the time-scales over which they operate.
The following images show the diversity of contexts in which this approach can be undertaken.
Moorea is an island located in French Polynesia. It’s known for its extraordinary marine biodiversity, but also for the great, natural spatial and temporal variability due to recurrent external forces. This place, and the statistical challenges it represents, has provided us with a wealth of inspiration in formulating our Progressive-Change BACIPS approach to environmental impact assessment.
Unlike classic experimental studies like this one, environmental impacts are not (and often should not) be replicated.
Recurrent disturbances such as Crown-of-Thorns Starfish (Acanthaster planci) outbreaks are important drivers of declines and recoveries in coral reef ecosystems. How can we reliably estimate the effect of local human interventions (for example marine protected areas, MPAs) amid such noise?
The challenge faced by ecologists when conducting impact assessments is to compare the state of the ecosystem in the presence of the intervention with the state of the system that would have existed if the intervention never occurred. This requires scientists to collect data before the intervention.
Here, a scientist is counting fish where a MPA will be implemented using a Diver-Operated Video system. Repeated assessments before enforcement provide an estimate of the spatial variability between the Control and Impact sites in the absence of an effect of the MPA.
A change in the difference in density between the Control and Impact sites after the establishment of the MPA provides an estimate of the local effect of the MPA. This is the BACIPS design.
Progressive-Change BACIPS uses these data to inform the form of the final model. Many models can be tested such as step-change, linear, asymptotic or logistic models – whatever that seems appropriate. This coral reef application was just one of the many possibilities to measure environmental impacts that our tool can reveal when applied to BACIPS data.
We have also applied it to other study contexts – such as the effect of highway construction on the abundance of birds. Here is an Andean condor (Vultur gryphus) flying away after the passage of a car.
This method is also well suited to forest ecosystems, for example to study the effect of increasing tourist visitation on this ancient Araucaria (Araucaria araucana) forest in Chile.
As long as data collected before and after, inside and outside the impacted area, exist Progressive-Change BACIPS is an excellent statistical approach to estimate the effects of environmental impacts.
Around the world there are concerns over the impacts of land use change and the developments (such as wind farms). These concerns have led to the implementation of tracking studies to better understand movement patterns of animals. Such studies have provided a wealth of high-resolution data and opportunities to explore sophisticated statistical methods for analysis of animal behaviour.
We use accelerometer data from aerial (Verreaux’s eagle in South Africa) and marine (blacktip reef shark in Hawai’i) systems to demonstrate the use of hidden Markov models (HMMs) in providing quantitative measures of behaviour. HMMs work really well for analysing animal accelerometer data because they account for serial autocorrelation in data. They allow for inferences to be made about relative activity and behaviour when animals cannot be directly observed too, which is very important.
In addition to this, HMMs provide data-driven estimates of the underlying distributions of the acceleration metrics – and the probability of switching between states – possibly as a function of covariates. The framework that we provide in ‘Analysis of animal accelerometer data using hidden Markov models‘ can be applied to a wide range of activity data. It opens up exciting opportunities for understanding drivers of individual animal behaviour.
Verreaux’s eagles lay one to two eggs but only raise one chick. These are normally hatch during mid-winter and stay on the nest for around 90 days. The parents provision the chick during this time, delivering prey to the nest. Rock hyrax is a frequent item on the menu, as seen here. Having concurred data from accelerometers and nest cameras for the same bird could allow for activity level to be linked to successful prey acquisition or prey type. (Photo taken by a nest camera installed as part of the Black Eagle Project, Megan Murgatroyd)
Wetlands tend to accumulate considerable anthropogenic pollution.
All living organisms are dependent on trace elements (TEs), including metals, that are acquired in very small quantities through their environment or diet. Most TEs are essential for growth, development and physiology of the organism, but excessive intake can be detrimental for animals and plants. Some TEs – especially heavy metals such as mercury, cadmium, lead and others – are generally toxic though. This toxicity occurs because species’ natural mechanisms fail to excrete excess TEs quickly enough for their metabolism to cope. TEs are present in the environment at different concentrations, either through natural processes or anthropogenic processes (i.e. pollution).
Some natural environments are more vulnerable to toxic effects of TEs. For instance, wetlands are geochemical endpoints of large river systems that often flow near or through cities, roads, factories, industries, cultivated lands, and/or mines, so they tend to accumulate considerable anthropogenic pollution. Vulnerable habitats like wetlands need to be closely monitored in order to assess the environmental health of these ecosystems. For this kind of monitoring we need reliable methods to measure TEs exposure, intake and bioaccumulation. Continue reading →
Bush-crickets are a little-known group of insects that inhabit our marshes, grasslands, woods, parks and gardens. Some may be seen in the summer when they are attracted to artificial lights, but as most produce noises that are on the edge of human hearing, we know little about their status. There are suggestions that some bush-crickets may be benefiting from climate change, while others may be affected by habitat changes. But how to survey something that is difficult to see and almost impossible to hear? Continue reading →
For the first time, it is possible to integrate at the global scale the results obtained with the most widely used methods to evaluate the “health” of ecosystems using lichens. This is the result of a study now published in the journal Methods in Ecology and Evolution, and represents a fundamental step for this indicator to be considered at the global scale and included in the list of indicators of the United Nations.
Lichens have long been successfully used by scientists as ecological indicators – a kind of environment health thermometer. These complex organisms – the yellow or green taints we often see on the surface of tree trunks – are very sensitive to pollution and changes in temperature and humidity. Evaluating how many lichens, of what kind, and their abundance in a certain ecosystem allows scientists to understand the impact that problems like climate change or pollution have on those ecosystems. Continue reading →
A search of almost any topic on Google Scholar promises to return tens of thousands of hits in less than a second. The first step in any research endeavour is to wade through the titanic amounts of articles available to become acquainted with the existing knowledge. For many people it’s one of the most dreadful and tedious parts of the scientific process.
But what if we could streamline/facilitate this step by automatizing parts of it? Automated content analysis (ACA) gives us the opportunity to do just that. ACA – a text-mining method that uses text-parsing and machine learning – is able to classify vast amounts of text into categories of named concepts. It can then quantify the frequency of those concepts and the relationships among them. Continue reading →
It’s not easy to characterise the local environment of species living in mountains because these habitats are highly heterogeneous. At a large scale, we typically assume that temperature varies with altitude, but at a local scale, we understand that exposure to wind or being in the shade has a great influence on climatic conditions. If you go from the south-facing to the north-facing side of a mountain, it can be easily 5°C colder. If we can feel that, so can the organisms that live up there. Plants in particular are submitted to tremendous climatic variations over a year. What we want to know is: how did they adapt to these climatic variations and how localised is their adaptation?
Overcoming the Challenges of Measuring Local Adaptation
We don’t know much about how organisms adapt locally because it’s so difficult to measure the environmental conditions that these plants are facing. Existing weather stations can’t capture micro-habitat conditions because they are few and far between. What we can do instead, is use topographic models of mountains to model their environment. After all, if orientation, slope or shade have an impact on climatic conditions, why couldn’t we use them to model local variations in temperature for example? Continue reading →