Environmental factors influencing aggregation of manta rays (Manta birostris) off the northeastern coast of the Yucatan Peninsula          下载免费PDF全文

Ana Hacohen‐Domené  Raúl O. Martínez‐Rincón  Felipe Galván‐Magaña  Natalí Cárdenas‐Palomo  Jorge Herrera‐Silveira 《Marine Ecology》2017,38(3)

Manta rays inhabit tropical, subtropical and temperate waters. Aggregation sites of manta rays have been recognized worldwide, but the reasons for this behavior are still poorly understood. This study describes environmental factors influencing aggregation sites of the giant manta ray (Manta birostris) off the northeastern coast of the Yucatan Peninsula. Observations of manta rays were obtained from scientific surveys conducted during 2006–2011. Environmental data were obtained from satellite imagery. The maximum entropy (Maxent) method for habitat modeling was used to determine the effects of environmental conditions on the species and predict suitable habitat for manta rays in this region. Primary productivity and distance to the coast were the most influential variables, suggesting that aggregation occurs in highly productive coastal waters. The distribution of manta rays predicted by the Maxent model showed that the most suitable habitat within the study area is located off the northeastern coast of the Yucatán Peninsula, more precisely, northeast of Isla Holbox and northwest of Isla Contoy. Seasonal patterns of distribution suggest that the most suitable conditions are present from July through September.  相似文献   

Evaluation of inductive logic programming for information extraction from natural language texts to support spatial data recommendation services     

Domen Smole  Marjan Čeh  Tomaž Podobnikar 《International journal of geographical information science》2013,27(11):1809-1827

In this article we analyze a well-known and extensively researched problem: how to find all datasets, on the one hand, and on the other hand only those that are of value to the user when dealing with a specific spatially oriented task. In analogy with existing approaches to a similar problem from other fields of human endeavor, we call this software solution ‘a spatial data recommendation service.’ In its final version, this service should be capable of matching requests created in the user's mind with the content of the existing datasets, while taking into account the user's preferences obtained from the user's previous use of the service. As a result, the service should recommend a list of datasets best suited to the user's needs. In this regard, we consider metadata, particularly natural language definitions of spatial entities, a crucial piece of the solution. To be able to use this information in the process of matching the user's request with the dataset content, this information must be semantically preprocessed. To automate this task we have applied a machine learning approach. With inductive logic programming (ILP) our system learns rules that identify and extract values for the five most frequent relations/properties found in Slovene natural language definitions of spatial entities. The initially established quality criterion for identifying and extracting information was met in three out of five examples. Therefore we conclude that ILP offers a promising approach to developing an information extraction component of a spatial data recommendation service.  相似文献