Rhyne A.L.,John H Prescott Marine Laboratory |
Rhyne A.L.,Roger Williams University |
Tlusty M.F.,John H Prescott Marine Laboratory |
Tlusty M.F.,University of Massachusetts Boston |
And 3 more authors.
Current Opinion in Environmental Sustainability | Year: 2014
Coral reefs are at the brink of a global, system-wide collapse. Human populations living at the water's edge are a vital key to the long-term survival and maintenance of these global biodiversity hotpots. Global trade combined with high levels of poverty threatens to siphon out biodiversity riches from developing nations to the developed world for short-term gains. The difficult challenge for local governance, conservationists, and resource managers alike is to create and maintain as diverse and well-functioning a Coral Reef Socio-Ecological System (CRSES) as possible. A fundamental shift in the structure of business practices, incentives and values are needed to move the marine aquarium trade to a more sustainable state. Rapid growth in the cultured coral trade and better fishery management in small fisheries are bright spots in the marine aquarium trade, and demonstrate that this trade can be part of a broader solution to reef conservation. © 2013 Elsevier B.V.
Sheil D.,Southern Cross University of Australia |
Sheil D.,Kabale University |
Sheil D.,Center for International Forestry Research |
Mugerwa B.,Kabale University |
Fegraus E.H.,Betty and Gordon Moore Center for Ecosystem Science and Economics
South African Journal of Wildlife Research | Year: 2013
The use of camera traps for wildlife research and monitoring is increasing and this is yielding significant observations at an accelerating pace. Yet many potentially valuable observations are overlooked, misinterpreted or withheld. Using our first-ever images of a wild African golden cat (Caracal aurata) catching prey, we consider practical challenges and opportunities for more effective image management systems. In particular we highlight the benefits of online image archives and assessments.
Selig E.R.,Betty and Gordon Moore Center for Ecosystem Science and Economics |
Longo C.,National Center for Ecological Analysis And Synthesis |
Halpern B.S.,National Center for Ecological Analysis And Synthesis |
Halpern B.S.,University of California at Santa Barbara |
And 8 more authors.
PLoS ONE | Year: 2013
People value the existence of a variety of marine species and habitats, many of which are negatively impacted by human activities. The Convention on Biological Diversity and other international and national policy agreements have set broad goals for reducing the rate of biodiversity loss. However, efforts to conserve biodiversity cannot be effective without comprehensive metrics both to assess progress towards meeting conservation goals and to account for measures that reduce pressures so that positive actions are encouraged. We developed an index based on a global assessment of the condition of marine biodiversity using publically available data to estimate the condition of species and habitats within 151 coastal countries. Our assessment also included data on social and ecological pressures on biodiversity as well as variables that indicate whether good governance is in place to reduce them. Thus, our index is a social as well as ecological measure of the current and likely future status of biodiversity. As part of our analyses, we set explicit reference points or targets that provide benchmarks for success and allow for comparative assessment of current conditions. Overall country-level scores ranged from 43 to 95 on a scale of 1 to 100, but countries that scored high for species did not necessarily score high for habitats. Although most current status scores were relatively high, likely future status scores for biodiversity were much lower in most countries due to negative trends for both species and habitats. We also found a strong positive relationship between the Human Development Index and resilience measures that could promote greater sustainability by reducing pressures. This relationship suggests that many developing countries lack effective governance, further jeopardizing their ability to maintain species and habitats in the future. © 2013 Selig et al.
Estes L.D.,Princeton University |
Bradley B.A.,University of Massachusetts Amherst |
Beukes H.,The Water Council |
Hole D.G.,Betty and Gordon Moore Center for Ecosystem Science and Economics |
And 5 more authors.
Global Ecology and Biogeography | Year: 2013
Aim: Intercomparison of mechanistic and empirical models is an important step towards improving projections of potential species distribution and abundance. We aim to compare suitability and productivity estimates for a well-understood crop species to evaluate the strengths and weaknesses of mechanistic versus empirical modelling. Location: South Africa. Methods: We compared four habitat suitability models for dryland maize based on climate and soil predictors. Two were created using maximum entropy (MAXENT), the first based on national crop distribution points and the second based only on locations with high productivity. The third approach used a generalized additive model (GAM) trained with continuous productivity data derived from the satellite normalized difference vegetation index (NDVI). The fourth model was a mechanistic crop growth model (DSSAT) made spatially explicit. We tested model accuracy by comparing the results with observed productivity derived from MODIS NDVI and with observed suitability based on the current spatial distribution of maize crop fields. Results: The GAM and DSSAT results were linearly correlated to NDVI-measured yield (R2 = 0.75 and 0.37, respectively). MAXENT suitability values were not linearly related to yield (R2 = 0.08); however, a MAXENT model based on occurrences of high-productivity maize was linearly related to yield (R2 = 0.62). All models produced crop suitability maps of similarly good accuracy (Kappa = 0.73-75). Main conclusions: These findings suggest that empirical models can achieve the same or better accuracy as mechanistic models for predicting both suitability (i.e. species range) and productivity (i.e. species abundance). While MAXENT could not predict productivity across the species range when trained on all occurrences, it could when trained with a high-productivity subset, suggesting that ecological niche models can be adjusted to better correlate with species abundance. © 2013 John Wiley & Sons Ltd.