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)