Coral reef quality and recreation fees in marine protected areas

Wielgus, Jeffrey; Balmford, Andrew; Lewis, Tiffany; Mora, Camilo; Gerber, Leah R.

doi:10.1111/j.1755-263x.2009.00084.x

Cited by 26 publications

(12 citation statements)

References 18 publications

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“…P. hawkins & roberts, 1993;J. P. hawkins et al, 1999;rouphael & Inglis, 1997;tratalos & Austin, 2001), contingent valuation methods to estimate revenues generated by the dive industry , potential for diving fees to contribute to management costs thur, 2010;Wielgus, balmford, Lewis, mora, & gerber, 2010), and recreational carrying capacity for scuba divers (Davis & tisdell, 1995;Di Franco, marchini, baiata, milazzo, & Chemello, 2009). however, data on divers' experiences, preferences, and level of satisfaction regarding different dive opportunities are more limited (macCarthy, O'Neill, & Williams, 2006;musa, 2002;musa, Kadir, & Lee, 2006;Sorice, Oh, & Ditton, 2007).…”

Section: Introductionmentioning

confidence: 99%

Resource Attributes That Contribute to Nonresident Diver Satisfaction in the Florida Keys, Usa

Paterson¹,

Young²,

Loomis³

et al. 2012

Tourism in Marine Environments

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Scuba diving is a popular form of marine-based tourism contributing over $1 billion annually to the tourism industry in the Florida Keys. this research explores diver characteristics, how segmentation into meaningful subgroups can be achieved, and how various factors affect satisfaction. Data were collected from nonresident divers and a 10-item index was created based on respondents' satisfaction. Discrepancy was calculated using the same items, and a specialization index was used to subgroup the respondents. Data were analyzed using step-wise regression. results revealed that discrepancies for individual items contributed to satisfaction, which differed according to specialization level. this suggests that satisfaction is related to both discrepancies between expectations and experiences, and specialization level, providing avenues for strategic marketing and management.

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Section: Introductionmentioning

confidence: 99%

Resource Attributes That Contribute to Nonresident Diver Satisfaction in the Florida Keys, Usa

Paterson¹,

Young²,

Loomis³

et al. 2012

Tourism in Marine Environments

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“…Coral reefs are under multiple threats from global climate changes, mainly oceanic acidification and warming [12], blast and cyanide fishing [13], coral collection for the marine aquaria trade [14], sunscreen use [15], light pollution [16], and excessive SCUBA diving pressure [17]. Wastewater and fertilizer discharge creates the phenomenon of anthropogenic eutrophication, or over-fertilization of the reef water with excess nutrients that can harm reefs by encouraging excess seaweed growth.…”

Section: The Future Of Coral Reefsmentioning

confidence: 99%

Neural Network Recognition of Marine Benthos and Corals

et al. 2020

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We present thorough this review the developments in the field, point out their current limitations, and outline its timelines and unique potential. In order to do so we introduce the methods used in each of the advances in the application of deep learning (DL) to coral research that took place between the years: 2016–2018. DL has unique capability of streamlining the description, analysis, and monitoring of coral reefs, saving time, and obtaining higher reliability and accuracy compared with error-prone human performance. Coral reefs are the most diverse and complex of marine ecosystems, undergoing a severe decline worldwide resulting from the adverse synergistic influences of global climate change, ocean acidification, and seawater warming, exacerbated by anthropogenic eutrophication and pollution. DL is an extension of some of the concepts originating from machine learning that join several multilayered neural networks. Machine learning refers to algorithms that automatically detect patterns in data. In the case of corals these data are underwater photographic images. Based on “learned” patterns, such programs can recognize new images. The novelty of DL is in the use of state-of-art computerized image analyses technologies, and its fully automated methodology of dealing with large data sets of images. Automated Image recognition refers to technologies that identify and detect objects or attributes in a digital video or image automatically. Image recognition classifies data into selected categories out of many. We show that Neural Network methods are already reliable in distinguishing corals from other benthos and non-coral organisms. Automated recognition of live coral cover is a powerful indicator of reef response to slow and transient changes in the environment. Improving automated recognition of coral species, DL methods already recognize decline of coral diversity due to natural and anthropogenic stressors. Diversity indicators can document the effectiveness of reef bioremediation initiatives. We explored the current applications of deep learning for corals and benthic image classification by discussing the most recent studies conducted by researchers. We review the developments in the field, point out their current limitations, and outline their timelines and unique potential. We also discussed a few future research directions in the fields of deep learning. Future needs are the age detection of single species, in order to track trends in their population recruitment, decline, and recovery. Fine resolution, at the polyp level, is still to be developed, in order to allow separation of species with similar macroscopic features. That refinement of DL will allow such comparisons and their analyses. We conclude that the usefulness of future, more refined automatic identification will allow reef comparison, and tracking long term changes in species diversity. The hitherto unused addition of intraspecific coral color parameters, will add the inclusion of physiological coral responses to environmental conditions and change thereof. The core aim of this review was to underscore the strength and reliability of the DL approach for documenting coral reef features based on an evaluation of the currently available published uses of this method. We expect that this review will encourage researchers from computer vision and marine societies to collaborate on similar long-term joint ventures.

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“…The application of user fees to fund environmental conservation is well established within National Parks, and when managed correctly can generate significant gains in environmental protection (Thur, 2010;Wielgus et al, 2010). Implementing user fees requires an understanding of how users value, and benefit from, ecosystem quality, to enable fees to reflect user preferences.…”

Section: Introductionmentioning

confidence: 99%

User fees across ecosystem boundaries: Are SCUBA divers willing to pay for terrestrial biodiversity conservation?

Roberts

Hanley

Cresswell

2017

Journal of Environmental Management

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While ecological links between ecosystems have been long recognised, management rarely crosses ecosystem boundaries. Coral reefs are susceptible to damage through terrestrial run-off, and failing to account for this within management threatens reef protection. In order to quantify the extent to that coral reef users are willing to support management actions to improve ecosystem quality, we conducted a choice experiment with SCUBA divers on the island of Bonaire, Caribbean Netherlands. Specifically, we estimated their willingness to pay to reduce terrestrial overgrazing as a means to improve reef health. Willingness to pay was estimated using the multinomial, random parameter and latent class logit models. Willingness to pay for improvements to reef quality was positive for the majority of respondents. Estimates from the latent class model determined willingness to pay for reef improvements of between $31.17 - $413.18/year, dependent on class membership. This represents a significant source of funding for terrestrial conservation, and illustrates the potential for user fees to be applied across ecosystem boundaries. We argue that such across-ecosystem-boundary funding mechanisms are an important avenue for future investigation in many connected systems.

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Coral reef quality and recreation fees in marine protected areas

Cited by 26 publications

References 18 publications

Resource Attributes That Contribute to Nonresident Diver Satisfaction in the Florida Keys, Usa

Resource Attributes That Contribute to Nonresident Diver Satisfaction in the Florida Keys, Usa

Neural Network Recognition of Marine Benthos and Corals

User fees across ecosystem boundaries: Are SCUBA divers willing to pay for terrestrial biodiversity conservation?

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