Complexity Increases Predictability in Allometrically Constrained Food Webs.

All ecosystems are subjected to chronic disturbances, such as harvest, pollution, and climate change. The capacity to forecast how species respond to such press perturbations is limited by our imprecise knowledge of pairwise species interaction strengths and the many direct and indirect pathways along which perturbations can propagate between species. Network complexity (size and connectance) has thereby been seen to limit the predictability of ecological systems. Here we demonstrate a counteracting mechanism in which the influence of indirect effects declines with increasing network complexity when species interactions are governed by universal allometric constraints. With these constraints, network size and connectance interact to produce a skewed distribution of interaction strengths whose skew becomes more pronounced with increasing complexity. Together, the increased prevalence of weak interactions and the increased relative strength and rarity of strong interactions in complex networks limit disturbance propagation and preserve the qualitative predictability of net effects even when pairwise interaction strengths exhibit substantial variation or uncertainty.

An online database for informing ecological network models: http://kelpforest.ucsc.edu.

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).

More than a meal integrating non-feeding interactions into food webs.

Organisms eating each other are only one of many types of well documented and important interactions among species. Other such types include habitat modification, predator interference and facilitation. However, ecological network research has been typically limited to either pure food webs or to networks of only a few (<3) interaction types. The great diversity of non-trophic interactions observed in nature has been poorly addressed by ecologists and largely excluded from network theory. Herein, we propose a conceptual framework that organises this diversity into three main functional classes defined by how they modify specific parameters in a dynamic food web model. This approach provides a path forward for incorporating non-trophic interactions in traditional food web models and offers a new perspective on tackling ecological complexity that should stimulate both theoretical and empirical approaches to understanding the patterns and dynamics of diverse species interactions in nature.

Asian fish tapeworm Bothriocephalus acheilognathi in the desert southwestern United States.

The Asian fish tapeworm Bothriocephalus acheilognathi (Cestoda: Bothriocephalidea) is an introduced fish parasite in the southwestern United States and is often considered a serious threat to native desert fishes. Determining the geographic distribution of nonnative fish parasites is important for recovery efforts of native fishes. We examined 1,140 individuals belonging to nine fish species from southwestern U.S. streams and springs between January 2005 and April 2007. The Asian fish tapeworm was present in the Gila River, Salt River, Verde River, San Pedro River, Aravaipa Creek, and Fossil Creek, Arizona, and in Lake Tuendae at Zzyzx Springs and Afton Canyon of the Mojave River, California. Overall prevalence of the Asian fish tapeworm in Arizona fish populations was 19% (range = 0-100%) and varied by location, time, and fish species. In California, the prevalence, abundance, and intensity of the Asian fish tapeworm in Mohave tui chub Gila bicolor mohavensis were higher during warmer months than during cooler months. Three new definitive host species--Yaqui chub G. purpurea, headwater chub G. nigra, and longfin dace agosia chrysogaster--were identified. Widespread occurrence of the Asian fish tapeworm in southwestern U.S. waters suggests that the lack of detection in other systems where nonnative fishes occur is due to a lack of effort as opposed to true absence of the parasite. To limit further spread of diseases to small, isolated systems, we recommend treatment for both endo- and exoparasites when management actions include translocation of fishes.