Habitat of Ants

Using Minnesotan Ants in a Biodiversity Model -- A spatial explicit approach to model ant biodiversity under local competition --

Background

Conservation Biology is an applied science. One goal of this integrated discipline is to clarify the issue and seek the solution to preserve biodiversity, while most natural communities contain a significant amount of species diversity which are endangered by modernization and globalization of human development. The biodiversity of ecological community provides an important understanding with a number of implications for ecosystem functioning and stability. A great number of work has been done in both experimental methods (field observation, field manipulation and laboratory experiments) and theoretical methods, such as the analysis of mathematical and computer-based models.

Since species diversity is accepted as one of the most important descriptive parameters in an ecological community, the central question of Conservation Biology is raised: Why are there so many kinds of organisms? We can view the number of species in co-existence in communities as a complex function of local spatial interaction (mainly competition of limited resource, with symbiosis, parasitism and other interactions) under evolutionary selection pressure and adaptive radiations with biogeographical barriers. MacArthur and Wilson (1967) examined how the rates of colonization and extinction might influence the species number within islands. With greatly over-simplification, larger area may contain more available niche to support species diversity. The other factor to affect species abundance distribution within community is succession time under ecological constraint, if we assume that tropical ecosystems are older than the other ecosystems, and arctic environments are more physically shaped than tropical climax communities. Productivity hypothesis also suggested diversity may be a function of community productivity because primary productivity provides the basis of resource (Brown 1973). Finally, the hypothesis of spatial complexity claimed that diversity may be explained in terms of environmental mosaics or spatial subdivision (Tilman 1994): the more complex and heterogeneous an environment is, the more niche it may offer to support diverse fauna and flora.

However, the content of biodiversity can be enriched by the role of biotic interactions (Sander 1968). It suggested the increase of biotic interactions would promote diversity. For example, predation can maintain diversity within community, even though predator can eliminate prey species. Interspecific competition also promotes biodiversity by increasing specialization and decreasing niche width, which leads to overlapping of co-existence or allows more species share or partition resource before exclusion by severe competition. In addition, intraspecific competition can also serve as a principal mechanism of evolutionary selection. It is generally essential to consider both interspecific and intraspecific competitions shaping biodiversity in community. From the non-spatial mathematical model of Lotka-Volterra model (Volterra 1926, Lotka 1932), coexistence to enhance biodiversity is only possible if intraspecific competition is more important than interspecific competition. This may be questionable under the spatial mechanism of co-existence, because local selection pressure of intraspecific competition can react with interspecific neighborhood in a spatial explicit sense. Local population maintains active range and determines the adjacent impact toward the other adjacent species. To maintain biodiversity, space and local distributed pattern are also important.

Objectives of Modeling Biodiversity

To examine the spatial roles of different competition, I choose ant fauna to study biodiversity because these social insects have rich behavioral interactions in both colony (intraspecific) and species (intraspecific) levels. Currently, the spatial explanation of species diversity distribution within community is still needed to expand. From classical ecological theory, the stable co-existing species are limited by resource and environmental uncertainty. This poses a dilemma: How does ant community maintain biodiversity spatially in a dynamic of interspecific and intraspecific competitions? What is the implication to Conservation Biology from this spatially interactive mechanism of ant biodiversity? Through the spatial explicit simulation and modeling, I propose to study what is the trade-off effect of ant biodiversity (coexistence) between interspecific and intraspecific competitions under different levels of environmental resource.

My First Model Description (co-existing competition model of ants)

This spatial explicit model is built with a grid system and each cell acts as a spatial unit to observe ant species coexistence. Within site, there is a frame-based component to allow multiple ant species to occupy a single patch with interspecific interaction. Between sites, different ant species compete with adjacent neighborhood (interspecies), or interact with different colonies of the same species (intercolonies or intraspecies). The competition among different species and colonies can be gradient between exclusive and non-exclusive levels, which depends on specific ant density and local resource abundance.

Assumptions

1. Simple competitors between sites
From my pilot observation and data in first year (2000), when available food appears, ants can be roughly classified as early arriver, late overwhelmer, occasional patroller and cryptic species. I assume early ant arrivers come from local patch because the spatial convenience leads to their rapid response. Within one bait station, it may or may not have the other late overwhelmer species from neighborhood to compete food. Thus the same ant species can be early arriver or late overwhelmer, which based on the spatial location and distribution pattern of their nest sites.
In my first stage of modeling, I only consider the first two competitors in grid system. To simplify the ant interactions in the beginning of model building, I assume occasional patroller and cryptic species are NOT significant important in competition. Generally, it is true for some cryptic species, such as thief ant (Solenosis molesta ) can only steals little pieces of food without causing a strong competition.
I wish to point out here it may not be true for some large and aggressive ants. For example, Camponotus noveboracensis is a beautiful red-black large carpenter ant which travels for a long distance to patrol its active range. Even small number of individuals can strongly attack the local abundant ants and cause severe mortality of small ants like Tapinoma sessile and Lasius aliens. In addition, it is needed to release this assumption in the future in order to consider all ant species in the further biodiversity modeling.

2. Resolution is suitable to model the continuous space: One cell can carry multiple species, but only one colony per species is allowed per cell.

3. Eliminate the complex polygeny (many queens in a colony) and polydomy (many nests or entrances in a colony) situation: All different sites in grid system occupied by the same ant species are treated for different colonies. This colony unit operates intraspecific competition separately.

Output of Model Simulation

1. Species richness: plus other biodiversity index with different weight of relative abundance (e.g. Shannon and Weaver index, Simpson index...etc.)
2. Species coexistence pattern: Where is the biodiversity hot spot to preserve? Why?
3. Species-abundance curve: Stable ecosystem or many endangered species?


Conclusion

It is a start to look at how the complex biodiversity system works in community scale from an emphasis of competitive interaction. The knowledge and insight from this model will help conservation biologists focus on the a number of key variables to explain the phenomena of ant coexistence which is regulated by a series of unique spatially interactive mechanisms. The model prediction will also allow us to apply behavioral indicators to enrich diversity index, and develop better estimates of " biodiversity " by concerning more biotic interaction in a spatial ecological network.