M. Julian Caley

Transferring biodiversity models for conservation: Opportunities and challenges

After decades of extensive surveying, knowledge of the global distribution of species still remains inadequate for many purposes. In the short to medium term, such knowledge is unlikely to improve greatly given the often prohibitive costs of surveying and the typically limited resources available. By forecasting biodiversity patterns in time and space, predictive models can help fill critical knowledge gaps and prioritise research to support better conservation and management. The ability of a model to predict biodiversity metrics in novel environments is termed “transferability,” and models with high transferability will be the most useful in this context. Despite their potentially broad utility, little guidance exists on what confers high transferability to biodiversity models. We synthesise recent advances in biodiversity model transfers to facilitate increased understanding of what underpins successful model transferability, demonstrating that a consistent approach has so far been lacking but is essential for achieving high levels of repeatability, transparency and accountability of model transfers. We provide a set of guidelines to support efficient learning and the improvement of model transferability.

Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience

Better mitigation of anthropogenic stressors on marine ecosystems is urgently needed to address increasing biodiversity losses worldwide. We explore opportunities for stressor mitigation using whole-of-systems modelling of ecological resilience, accounting for complex interactions between stressors, their timing and duration, background environmental conditions and biological processes. We then search for ecological windows, times when stressors minimally impact ecological resilience, defined here as risk, recovery and resistance. We show for 28 globally distributed seagrass meadows that stressor scheduling that exploits ecological windows for dredging campaigns can achieve up to a fourfold reduction in recovery time and 35% reduction in extinction risk. Although the timing and length of windows vary among sites to some degree, global trends indicate favourable windows in autumn and winter. Our results demonstrate that resilience is dynamic with respect to space, time and stressors, varying most strongly with: (i) the life history of the seagrass genus and (ii) the duration and timing of the impacting stress.Stressors such as sediment dredging can harm marine organisms, but this impact could be minimised if targeted within ‘ecological windows’. Here, Wu and colleagues develop a modelling framework to identify ecological windows that maximise seagrass resilience under varying dredging schedules.

Spatially balanced designs that incorporate legacy sites

1. A robust scientific conclusion is the result of a rigorous scientific process. In observational ecology, this process involves making inferences about a population from a sample. The sample is crucial, and is the result of implementing a survey design. A good survey design ensures that the data from the survey are capable of answering the research question. Better designs, such as spatially balanced designs, will also be as precise as possible given the constraints of the budget. 2. Many study areas will have previously sampled ‘legacy sites’ that already have accumulated a time series of observations. For estimating trent, it is often beneficial to include these sites within a new survey. In this paper, we propose a method to incorporate the locations of legacy sites into new spatially balanced survey designs to ensure spatial balance among all sample locations. 3. Simulation experiments indicate that incorporating the spatial location of legacy sites increases spatial balance and decreases uncertainty in inferences (smaller standard errors in mean estimates) when compared to designs that ignore legacy site locations. We illustrate the process of incorporating legacy sites using a proposed survey of a large marine reserve in South-Eastern Australia, although the method is applicable to all environments. 4. Our approach allows for integration of legacy sites into a new spatially balanced design, increasing efficiency. Scientists, managers and funders alike will benefit from this methodology – it provides a tool to provide efficient survey designs around established ones, including in-the-field adjustments. In this way, it can aid integrated monitoring programmes. An R-package that implements these methods, called MBHdesign, is available from CRAN.

Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks

Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We study the cumulative impacts of maintenance dredging on seagrass ecosystems as a canonical example. Maintenance dredging causes disturbances lasting weeks to months, often repeated at yearly intervals. We present a risk-based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN). Our approach estimates the impact of a hazard on a systems response in terms of resistance, recovery and persistence, commonly used to characterise the resilience of a system. We consider whole-of-system interactions including light reduction due to dredging (the hazard), the duration, frequency and start time of dredging, and ecosystem characteristics such as the life-history traits expressed by genera and local environmental conditions. The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). Although impacts varied by combinations of dredging parameters and the seagrass meadows being studied, in general, 3 months of duration or more, or repeat dredging every 3 or more years, were key thresholds beyond which resilience can be compromised. Additionally, managing light reduction to less than 50% can significantly decrease one or more of loss, recovery time and risk of local extinction, especially in the presence of cumulative stressors. Synthesis and applications. Our risk-based approach enables managers to develop thresholds by predicting the impact of different configurations of anthropogenic disturbances being managed. Many real-world maintenance dredging requirements fall within these parameters, and our results show that such dredging can be successfully managed to maintain healthy seagrass meadows in the absence of other disturbances. We evaluated opportunities for risk mitigation using time windows; periods during which the impact of dredging stress did not impair resilience.

Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora

Seawater temperature anomalies associated with warming climate have been linked to increases in coral disease outbreaks that have contributed to coral reef declines globally. However, little is known about how seasonal scale variations in environmental factors influence disease dynamics at the level of individual coral colonies. In this study, we applied a multi-state Markov model (MSM) to investigate the dynamics of black band disease (BBD) developing from apparently healthy corals and/or a precursor-stage, termed ‘cyanobacterial patches’ (CP), in relation to seasonal variation in light and seawater temperature at two reef sites around Pelorus Island in the central sector of the Great Barrier Reef. The model predicted that the proportion of colonies transitioning from BBD to Healthy states within three months was approximately 57%, but 5.6% of BBD cases resulted in whole colony mortality. According to our modelling, healthy coral colonies were more susceptible to BBD during summer months when light levels were at their maxima and seawater temperatures were either rising or at their maxima. In contrast, CP mostly occurred during spring, when both light and seawater temperatures were rising. This suggests that environmental drivers for healthy coral colonies transitioning into a CP state are different from those driving transitions into BBD. Our model predicts that (1) the transition from healthy to CP state is best explained by increasing light, (2) the transition between Healthy to BBD occurs more frequently from early to late summer, (3) 20% of CP infected corals developed BBD, although light and temperature appeared to have limited impact on this state transition, and (4) the number of transitions from Healthy to BBD differed significantly between the two study sites, potentially reflecting differences in localised wave action regimes.

Challenges of transferring models of fish abundance between coral reefs

Reliable abundance estimates for species are fundamental in ecology, fisheries, and conservation. Consequently, predictive models able to provide reliable estimates for un- or poorly-surveyed locations would prove a valuable tool for management. Based on commonly used environmental and physical predictors, we developed predictive models of total fish abundance and of abundance by fish family for ten representative taxonomic families for the Great Barrier Reef (GBR) using multiple temporal scenarios. We then tested if models developed for the GBR (reference system) could predict fish abundances at Ningaloo Reef (NR; target system), i.e., if these GBR models could be successfully transferred to NR. Models of abundance by fish family resulted in improved performance (e.g., 44.1% <R2 < 50.6% for Acanthuridae) compared to total fish abundance (9% <R2 < 18.6%). However, in contrast with previous transferability obtained for similar models for fish species richness from the GBR to NR, transferability for these fish abundance models was poor. When compared with observations of fish abundance collected in NR, our transferability results had low validation scores (R2 < 6%, p > 0.05). High spatio-temporal variability of patterns in fish abundance at the family and population levels in both reef systems likely affected the transferability of these models. Inclusion of additional predictors with potential direct effects on abundance, such as local fishing effort or topographic complexity, may improve transferability of fish abundance models. However, observations of these local-scale predictors are often not available, and might thereby hinder studies on model transferability and its usefulness for conservation planning and management.

Using virtual reality to estimate aesthetic values of coral reefs

Aesthetic value, or beauty, is important to the relationship between humans and natural environments and is, therefore, a fundamental socio-economic attribute of conservation alongside other ecosystem services. However, beauty is difficult to quantify and is not estimated well using traditional approaches to monitoring coral-reef aesthetics. To improve the estimation of ecosystem aesthetic values, we developed and implemented a novel framework used to quantify features of coral-reef aesthetics based on peoples perceptions of beauty. Three observer groups with different experience to reef environments (Marine Scientist, Experienced Diver and Citizen) were virtually immersed in Australians Great Barrier Reef (GBR) using 360° images. Perceptions of beauty and observations were used to assess the importance of eight potential attributes of reef-aesthetic value. Among these, heterogeneity, defined by structural complexity and colour diversity, was positively associated with coral-reef-aesthetic values. There were no group-level differences in the way the observer groups perceived reef aesthetics suggesting that past experiences with coral reefs do not necessarily influence the perception of beauty by the observer. The framework developed here provides a generic tool to help identify indicators of aesthetic value applicable to a wide variety of natural systems. The ability to estimate aesthetic values robustly adds an important dimension to the holistic conservation of the GBR, coral reefs worldwide and other natural ecosystems.

Prediction of solar irradiance using ray-tracing techniques for coral macro- and micro-habitats

Light distribution on coral reefs is very heterogeneous at the microhabitat level and is an important determinant of coral thermal microenvironments. This study implemented a solar load model that uses a backward ray-tracing method to estimate macroscale and microscale variations of solar irradiance penetrating the ocean surface and impacting the surfaces of coral colonies. We then explored whether morphological characteristics such as tissue darkness (or pigmentation) and thickness may influence the amount of light captured and its spectral distribution by two contrasting coral colony morphologies, branching and massive. Results of global horizontal irradiance above and below the sea-surface and at the surface of coral colonies were validated using spectrometer scans, field measurements, and empirical correlations. The macroscale results of horizontal, irradiated, and shaded irradiance levels and solar altitude angles for PAR, UVA and UVB compared very well with the spectrometer-based observations (typically within < 5%). In general, a comparison between the model results and field and empirical measurements indicated that the contributions of clouds, turbidity, and tides to variations in irradiance at various depth (up to 5 m) were typically within 5-10% of each other. Moreover, the effect of colony darkness or pigmentation on light microenvironment was notably more pronounced for the massive species than branching colony. This study provided insights that species with thinner tissue have the ability to intercept more light with the difference in terms of irradiance levels between 0.1 mm and 0.8 mm tissue thickness for both massive and branching colonies were approximately 2 W m-2, which was quite unlikely to influence the overall coral heat budgets.

Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef

Monitoring programs are essential for understanding patterns, trends, and threats in ecological and environmental systems. However, such programs are costly in terms of dollars, human resources, and technology, and complex in terms of balancing short- and long-term requirements. In this work, We develop new statistical methods for implementing cost-effective adaptive sampling and monitoring schemes for coral reef that can better utilize existing information and resources, and which can incorporate available prior information. Our research was motivated by developing efficient monitoring practices for Australias Great Barrier Reef. We develop and implement two types of adaptive sampling schemes, static and sequential, and show that they can be more informative and cost-effective than an existing (nonadaptive) monitoring program. Our methods are developed in a Bayesian framework with a range of utility functions relevant to environmental monitoring. Our results demonstrate the considerable potential for adaptive design to support improved management outcomes in comparison to set-and-forget styles of surveillance monitoring.