Many things can negatively affect stream ecosystems – water abstraction, eutrophication and fine sediment influx are just a few. However, only intact freshwater ecosystems can sustainably deliver the ecosystem services – such as particle filtration, food biomass production and the supply of drinking water – that we rely on. Because of this, stream management and restoration has often been in the focus of environmental legislation world-wide. Macrozoobenthic communities are often key biological components of stream ecosystems. As many taxa within these communities are sensitive to negative stressors introduced by humans, they’re ideal for assessing the quality of water.
Unfortunately, most macrozoobenthic taxa – such as stone-, may-, and caddisflies as well as most other invertebrates – are often found in juvenile larval life stages in these ecosystems, so they’re often difficult to identify based on morphology. With the DNA based metabarcoding method though, almost all taxa in a stream can be reliably identified up to species level using a standardised gene fragment. One key component of this strategy is the development of universal markers, which allow detection of the diverse macrozoobenthic groups.
Our new R package PrimerMiner provides a framework for obtaining sequence data from available reference databases and identifying suitable primer binding sites for marker amplification. The package makes this process quicker and easier. In the following pictures, we summarise the key steps of DNA metabarcoding.
Rivers are home to a wide range of macroinvertebrate species. These are often used to assess water quality as many respond sensitively to anthropogenic stressors, like organic pollution, eutrophication or fine sediment influx.
One macroinvertebrate sample can contain hundreds or even thousands of specimens.
Invertebrates are collected using standardised field sampling protocols and usually identified based on their morphology. The taxa lists obtained this way are then used to assess stream water quality based on associated bioindication values of the individual taxa. But identifying juvenile invertebrates by morphology isn’t possible for all collected taxa.
DNA based identification is a promising alternative to morphology based identification. Whole bulk samples with hundreds of organisms can be identified at once – often to species level – which can improve the accuracy of assessments.
All specimens in the sample have to be well homogenised for DNA extraction before we can move on to DNA metabarcoding. The specimens are easier to homogenise if they are dried first.
The dried samples are mechanically homogenised using a bead mill and sterile tubes (which are only used once).
After 30 minutes of grinding, all specimens are homogenised to fine powder, which can then used for DNA extraction.
The homogenized macroinvertebrate tissue is incubated in digestion buffer (a solution that dissolves cell membranes) to enable DNA extraction. Only a few mg (~10 – 20 mg) of the homogenate is used for DNA extraction, as the efficiency decreases when too much tissue is extracted at once.
We used the R package PrimerMiner to obtain sequences for the 15 most important freshwater invertebrates from reference databases. Edith Vamos and I are screening the sequence alignment for suitable primer binding sites in this picture.
The developed primers are tested and PCR optimised using both mock samples and complete communities.
After successful marker amplification, several uniquely tagged samples are pooled and then sent to an external service provider for high throughput sequencing. The sequences are then bioinformatically processed and compared to reference databases. These precisely identify the taxa in each sample and connect bioindication values to taxa lists and stream types.
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)