M. Julian Caley’s research while affiliated with James Cook University and other places

Understanding connectivity of marine species is crucial for their management. This connectivity, however, is difficult to quantify; propagules of marine species are typically small, numerous, and can travel large distances. Consequently, oceanographic models are often used to simulate larval dispersal. To avoid high computational costs, these models typically use, pooled or cross-sectional data and fixed biological parameters. Here we explore how temporal and spatial variation in current velocities, and pelagic larval duration and buoyancy can alter patterns of marine connectivity of Crown-of-Thorns starfish on Australia’s Great Barrier Reef. Our results reveal highly variable reef connectivity in space and time mediated by all three factors. No individual reef acted consistently as a population source or sink. It is, therefore, important to consider many factors concurrently when estimating connectivity for understanding these population dynamics, especially where such estimates are relied on for evidence-based decision making.

Competition among land uses is making it increasingly difficult to set aside adequate space for wildlife and nature conservation, so it is imperative that opportunities that simultaneously achieve commercial and conservation outcomes be identified and seized. Such opportunities exist in the renewable energy industry. It is widely recognized that renewable energy generation benefits the ecosphere through reduced carbon emissions, but currently, further opportunities for realising direct and indirect conservation benefits through the design of solar farms are less well known. Among other opportunities, solar farm designs that deliver environmental credits through carbon sequestration and biodiversity improvements can deliver higher financial returns. Other opportunities to improve local hydrology, pollination, and pest-control services could be available depending on site-specific characteristics where solar farms are built, and the other land use practices that exist, or are possible, in the immediate vicinity. Here, we explore opportunities among renewable energy generation, agriculture, and conservation, through the co-location and innovative design of PV solar energy farms on grazing and croplands. These forms of land sharing can achieve higher land-equivalent ratios (LERs), a quantitative metric of the reduction in land use. We identify opportunities whereby solar farms can be designed to improve biodiversity, land condition, and conservation outcomes, while maintaining or increasing commercial returns. Much work remains, however, to understand the suite of opportunities available for achieving simultaneously the best commercial and conservation outcomes through solar farm designs in agricultural landscapes.

Systematic conservation planning requires spatial information on biodiversity. Such information is often unavailable, forcing spatial planning to rely on assumed relationships between species and environmental features. This problem is particularly acute in large, remote marine protected areas that are proliferating rapidly. Here, we use models to predict whether (a) macrobenthic biodiversity across four taxa (gorgonians, soft corals, hard corals, and sponges) with different life histories are congruent within seascape features through regional space; and (b) models generated in an intensively‐sampled area in one region can predict the occurrence of habitat‐forming macrobenthos in neighboring ones. All four taxa studied showed similar habitat preferences, but high variability in distributions among and within features suggesting factors other than simple geomorphology influence these regional biodiversity patterns. Nonetheless, models derived from one region accurately predicted the presence and absence of the same taxa hundreds of kilometers away. This transferability of models of species occurrences has the potential to deliver improved reserve design in data‐deficient regions.

Abstract The relative roles of top‐down (consumer‐driven) and bottom‐up (resource‐driven) forcing in exploited marine ecosystems have been much debated. Examples from a variety of marine systems of exploitation‐induced, top‐down trophic forcing have led to a general view that human‐induced predator perturbations can disrupt entire marine food webs, yet other studies that have found no such evidence provide a counterpoint. Though evidence continues to emerge, an unresolved debate exists regarding both the relative roles of top‐down versus bottom‐up forcing and the capacity of human exploitation to instigate top‐down, community‐level effects. Using time‐series data for 104 reef communities spanning tropical to temperate Australia from 1992 to 2013, we aimed to quantify relationships among long‐term trophic group population density trends, latitude, and exploitation status over a continental‐scale biogeographic range. Specifically, we amalgamated two long‐term monitoring databases of marine community dynamics to test for significant positive or negative trends in density of each of three key trophic levels (predators, herbivores, and algae) across the entire time series at each of the 104 locations. We found that trophic control tended toward bottom‐up driven in tropical systems and top‐down driven in temperate systems. Further, alternating long‐term population trends across multiple trophic levels (a method of identifying trophic cascades), presumably due to top‐down trophic forcing, occurred in roughly fifteen percent of locations where the prerequisite significant predator trends occurred. Such alternating trophic trends were significantly more likely to occur at locations with increasing predator densities over time. Within these locations, we found a marked latitudinal gradient in the prevalence of long‐term, alternating trophic group trends, from rare in the tropics (

Global environmental change, such as ocean warming and increased cyclone activity, is driving widespread and rapid declines in the abundance of key ecosystem engineers, reef-building corals, on the Great Barrier Reef. Our ability to understand how coral associated species, such as reef fishes, respond to coral loss can be impeded by uncertainty surrounding natural spatio-temporal variability of coral populations. To address this issue, we developed a semi-parametric hierarchical Bayesian model to estimate long-term trajectories of habitat-forming coral cover as a function of three spatial scales (sub-region, habitat and site) and environmental disturbances. The relationships between coral cover trajectories and fish community structure were examined using posterior predictive distributions of estimated coral cover from the statistical model. In the absence of direct observations of fish community structure, we used the probability of coral cover being above some ecological threshold values as a proxy for potential disruptions of fish community structure. Threshold values were derived from published field studies that estimated changes in the structure of coral-reef fish communities and coral cover after major disturbances. In these studies, fish community structure did not change where post-disturbance coral cover was > 20%. Disruptions in the structure of these communities were observed when coral cover dropped to between 10–20% and declines in fish diversity were typical where coral cover ranged from between 5 and 10%. Based on these thresholds values, posterior probabilities of coral cover being above 20% and between 10 and 20% and between 5 and 10% were calculated across spatial scales on the Great Barrier Reef (GBR) from 1995 to 2011. At the GBR scale, probabilities of coral cover being above these thresholds remained relatively stable through time. Across years, probabilities of coral cover being at least > 20% remained null for the sub-regions of Cairns, Townsville, Whitsundays and Swain but highly variable between reef sites within these sub-regions, with the exception of Townsville. In the Townsville area, probabilities of coral cover being between 10–20% and 5–10% declined from 0.75 to 0 during the study period. This finding highlights potential sub-regional fish community structure disruptions which have not yet been observed at this spatial scale. As frequency and intensity of disturbance events continue to rise, and consequently, as coral cover declines further, the probabilistic Bayesian approach presented in this chapter could be used to help provide early warnings of major ecological shifts at management relevant scales in the absence of direct observations.

Predictive biodiversity distribution models (BDM) are useful for understanding the structure and functioning of ecological communities and managing them in the face of anthropogenic disturbances. In cases where their predictive performance is good, such models can help fill knowledge gaps that could only otherwise be addressed using direct observation, an often logistically and financially onerous prospect. The cornerstones of such models are environmental and spatial predictors. Typically, however, these predictors vary on different spatial and temporal scales than the biodiversity they are used to predict and are interpolated over space and time. We explore the consequences of these scale mismatches between predictors and predictions by comparing the results of BDMs built to predict fish species richness on Australia’s Great Barrier Reef. Specifically, we compared a series of annual models with uninformed priors with models built using the same predictors and observations, but which accumulated information through time via the inclusion of informed priors calculated from previous observation years. Advantages of using informed priors in these models included (1) down-weighting the importance of a large disturbance, (2) more certain species richness predictions, (3) more consistent predictions of species richness and (4) increased certainty in parameter coefficients. Despite such advantages, further research will be required to find additional ways to improve model performance.

Anticipating future changes of an ecosystem’s dynamics requires knowledge of how its key communities respond to current environmental regimes. The Great Barrier Reef (GBR) is under threat, with rapid changes of its reef‐building hard coral community structure already evident across broad spatial scales. While many of the underlying relationships between hard corals and multiple disturbances have been documented, responses of other benthic communities to disturbances are not well understood. Here, we used statistical modelling to explore the effects of broad‐scale climate‐related disturbances on benthic communities to predict their structure under scenarios of increasing disturbance frequency. We parameterized a multivariate model using the composition of benthic communities estimated by 145,000 observations from the northern GBR between 2012 and 2017. During this time, surveyed reefs were variously impacted by two tropical cyclones and two heat stress events that resulted in extensive hard coral mortality. This unprecedented sequence of disturbances was used to estimate the effects of discrete versus interacting disturbances on the compositional structure of hard corals, soft corals, and algae. Discrete disturbances increased the prevalence of algae relative to hard corals while the interaction between cyclones and heat stress was the main driver of the increase in soft corals relative to algae and hard corals. Predictions from disturbance scenarios included relative increases in algae versus soft corals that varied by the frequency and types of disturbance interactions. However, high un‐certainty of compositional changes in the presence of several disturbances shows that responses of algae and soft corals to the decline in hard corals needs further research. Better understanding the effects of multiple disturbances on benthic communities as a whole is essential for predicting the future status of coral reefs and managing them in the light of new environmental regimes. The approach we develop here opens new opportunities for reaching this goal. Model code available at: https://github.com/JulieVercelloni/multivariate_abundance_model_GBR

The Evidence Based Decision Making (EBDM) paradigm encourages managers to base their decisions on the strongest available evidence, but it has been criticized for placing too much emphasis on the choice of study design method without considering the types of questions that are being addressed as well as other relevant factors such as how well a study is implemented. Here we review the objectives of Australia’s Marine Park network, and identify the types of questions and data analysis that would address these objectives. Critically, we consider how the design of a monitoring program influences our ability to adequately answer these questions, using the strength of evidence hierarchy from the EBDM paradigm to assess the adequacy of different design strategies and other sources of information. It is important for conservation managers to recognize that the types of questions monitoring programs are able to answer depends on how they are designed and how the collected data are analyzed. The socio-political process that dictates where protected areas are placed typically excludes the strongest types of evidence, Random Controlled Trials (RCTs), for certain questions. Evidence bases that are stronger than ones commonly employed to date, however, could be used to provide a causal inference, including for those questions where RCTs are excluded, but only if appropriate designs such as cohort or case-control studies are used, and supported where relevant by appropriate sample frames. Randomized, spatially balanced sampling, together with careful selection of control sites, and more extensive use of propensity scores and structured elicitation of expert judgment, are also practical ways to improve the evidence base for answering the questions that underlie marine park objectives and motivate long-term monitoring programs.

Numerous organisations collect data in the Great Barrier Reef (GBR), but they are rarely analysed together due to different program objectives, methods, and data quality. We developed a weighted spatio-temporal Bayesian model and used it to integrate image-based hard-coral data collected by professional and citizen scientists, who captured and/or classified underwater images. We used the model to predict coral cover across the GBR with estimates of uncertainty; thus filling gaps in space and time where no data exist. Additional data increased the model's predictive ability by 43%, but did not affect model inferences about pressures (e.g. bleaching and cyclone damage). Thus, effective integration of professional and high-volume citizen data could enhance the capacity and cost-efficiency of monitoring programs. This general approach is equally viable for other variables collected in the marine environment or other ecosystems; opening up new opportunities to integrate data and provide pathways for community engagement/stewardship.

Natural environmental gradients encompass systematic variation in abiotic factors that can be exploited to test competing explanations of biodiversity patterns. The species-energy (SE) hypothesis attempts to explain species richness gradients as a function of energy availability. However, limited empirical support for SE is often attributed to idiosyncratic, local-scale processes distorting the underlying SE relationship. Meanwhile, studies are also often confounded by factors such as sampling biases, dispersal boundaries and unclear definitions of energy availability. Here, we used spatially structured observations of 8460 colonies of photo-symbiotic reef-building corals and a null-model to test whether energy can explain observed coral species richness over depth. Species richness was left-skewed, hump-shaped and unrelated to energy availability. While local-scale processes were evident, their influence on species richness was insufficient to reconcile observations with model predictions. Therefore, energy availability, either in isolation or in combination with local deterministic processes, was unable to explain coral species richness across depth. Our results demonstrate that local-scale processes do not necessarily explain deviations in species richness from theoretical models, and that the use of idiosyncratic small-scale factors to explain large-scale ecological patterns requires the utmost caution.