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Recent studies have provided theoretical and empirical evidence about the importance of hatchling production for sea turtle population dynamics. Therefore, understanding the effect of nesting habitat loss as a factor leading to hatchling reduction is essential in order to establish conservation plans for the recovery of sea turtle populations. In the present study, we developed a method to quantify habitat loss and link it with hatchling production. We used data for loggerhead sea turtles (Caretta caretta) collected at Sekania nesting beach, western Greece, to describe biological and behavioral attributes of nesting individuals. Spatial characteristics of the nesting site were analyzed and alternative scenarios of habitat loss were examined. We then used circle-packing technique to evaluate the impact of an increasingly reduced available nesting area on the spatial distribution of nests. An increased number of nests within the study site resulted in density-dependent processes regulating hatchling production. Under the different scenarios, we evaluated the risk of the laying nests exceeding the estimated carrying capacity of the nesting beach. Our results clearly demonstrated the need to apply direct and efficient conservation measures at Sekania nesting site to minimize further habitat loss from human-related processes and a rising sea level. The approach developed evaluates the effect of habitat loss upon nesting by linking it with quantifiable processes (density dependence), providing a conservation tool to guide planning decisions towards the conservation of the sea turtle population. 相似文献
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Identifying wildland fire ignition factors through sensitivity analysis of a neural network 总被引：6，自引：2，他引：4
Christos Vasilakos Kostas Kalabokidis John Hatzopoulos Ioannis Matsinos 《Natural Hazards》2009,50(1):125-143
Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through
standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the
robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating
system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus
civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions.
Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this
article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the
influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were
utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network’s
weights after the training procedure (i.e., the percentage of influence—PI, the weight product—WP, and the partial derivatives—PD
methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that
the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables
of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have
a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables’
importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude
of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires
to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence. 相似文献
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