Positive feedbacks driving habitat-forming species recovery and population growth are often lost as ecosystems degrade. For such systems, identifying mechanisms that limit the re-establishment of critical positive feedbacks is key to facilitating recovery. Theory predicts the primary drivers limiting system recovery shift from biological to physical as abiotic stress increases, but recent work has demonstrated that this seldom happens. We combined field and laboratory experiments to identify variation in limitations to coral recovery along an environmental stress gradient at Ningaloo Reef and Exmouth Gulf in northwest Australia. Many reefs in the region are coral depauperate due to recent cyclones and thermal stress. In general, recovery trajectories are prolonged due to limited coral recruitment. Consistent with theory, clearer water reefs under low thermal stress appear limited by biological interactions: competition with turf algae caused high mortality of newly settled corals and upright macroalgal stands drove mortality in transplanted juvenile corals. Laboratory experiments showed a positive relationship between crustose coralline algae cover and coral settlement, but only in the absence of sedimentation. Contrary to expectation, coral recovery does not appear limited by the survival or growth of recruits on turbid reefs under higher thermal stress, but to exceptionally low larval supply. Laboratory experiments showed that larval survival and settlement are unaffected by seawater quality across the study region. Rather, connectivity models predicted that many of the more turbid reefs in the Gulf are predominantly self seeded, receiving limited supply under degraded reef states. Overall, we find that the influence of oceanography can overwhelm the influences of physical and biological interactions on recovery potential at locations where environmental stressors are high, whereas populations in relatively benign physical conditions are predominantly structured by local ecological drivers. Such context-dependent information can help guide expectations and assist managers in optimizing strategies for spatial conservation planning for system recovery.

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A practical introduction to intelligent computer vision theory, design, implementation, and technology.

The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods-especially among adaboost varieties and particle filtering methods-have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including:

PRTools5 software for MATLAB-especially the latest representation and generalization software toolbox for PRTools5
Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods
The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods
All new coverage of the Adaboost and its implementation in PRTools5.

A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website. @en