An online database for informing ecological network models: http://kelpforest.ucsc.edu.
PLoS One
; 9(10): e109356, 2014.
Article
em En
| MEDLINE
| ID: mdl-25343723
ABSTRACT
Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database (http//kelpforest.ucsc.edu/) to facilitate the collation and dissemination of such information. Many of the database's attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link (https//github.com/kelpforest-cameo/databaseui).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Ecossistema
/
Internet
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article