Canopy
Removal
> deep water
> surface moisture
Water
Availability
Plant productivity
Herbivore
Diversity
Predator
Diveristy
Canopy
Retention
Fungus productivity
Fungivore
Diveristy

The model above is a diagram of the our experimental plot in the H.J.Andrews experimental forest during the summer of 2007. This site was comprised of adjacent open and closed canopy habitat in which we surveyed for terrestrial arthropods using pitfall traps. My specific investigation was involved hypothesis relating to species richness, and diversity of three functional groups: herbivores, predators, and fungivores.
The diagram is an attempt to model the components of the ecosystem that have influence on and are influenced by these ecosystem components.
The main habitat component that I used to differentiate my data collection sites was canopy closure. When looked at as a hierarchical model, I see land use policy as the main landscape level force that is impacting canopy closure. To a lesser extent fire, blowdown, floods, and disease will also create forest openings.
At the mosaic level of the hierarchy, I treated our survey area as two patches, one with a closed canopy and the other with an open canopy. Entire forested areas could be categorized into a mosaic of patches based on this single parameter. Mosaic level constraints impacting the productivity of the patches include water availability, aspect, elevation, temperature.
At the patch level, I saw the presence of downed woody debris as the major ecosystem component driving microhabitat differences, especially within the forest openings. The presence of downed wood has impacts on water availability and site moisture heterogeneity, food sources of fungi and detritivores, future sources of nutrient rich humus, and refuges for predators and fungivores.
Feedback in my system was demonstrated in three areas. Predators are self limiting. They tend to be territorial and many are generalist predators that would feed on other predators. This is shown with a self limiting feedback loop attached to the “predator diversity” node. Predators also serve as positive feedback loops on the fungus and plant nodes. By feeding on herbivores and fungivores they indirectly promote the growth of the plants and fungi.
One component of my system that is likely to change over a short period of time, and with annual regularity, is the surface moisture level. This moisture is greatly reduced during the driest months of the year (August / Sept). I would project that this time of year would show greatly reduced fungus and fungivore productivity in the upper layers of topsoil. this would, in turn have impacts on the predator population. Not pictured here, I also think that the impacts of the hot dry months negatively impact detritivore productivity as well as their predators. many plants are able to draw water from deeper layers of soil, bringing food and moisture to the surface for their herbivores, so this component would be less affected than the others.
At the bottom of my diagram I placed a disturbance node titles “long term effects of light fire disturbance”.
The immediate effects of a light fire would be to wipe out all of the above ground herbs and fungal hyphae found in the litter layer as well as the surface and immediately subsurface arthropods. Given the presence of large woody debris in the system as well as adjacent unaffected habitat, the burned patch would begin recolonization as the ground was cooling. Deep roots and seeds that survived the fire would begin regrowth as well. I do not see light fire as having a long term negative impact on the system. Rather, light fire would tend to create a nutrient release that would further drive the system by adding nutrients from the bottom.
This fresh source of nutrients would drive additional plant and fungus productivity, eventually causing rises in arthropods at all trophic levels.
The impacts of light fire on the arthropod populations could be tested in tow ways. In order to avoid the possibility of other habitat characteristics impacting your data, one could pitfall trap a patch, conduct a controlled burn at that patch, and then pitfall trap that patch again after the plant component showed clear regeneration (2-3 years??). On the other hand, in order to diminish the impacts of other unpredicted temporal changes to the arthropod community, one could conduct surveys simultaneously in both treated and untreated patches that showed great similarities in habitat characteristics prior to treatment. Species richness and diversity could then be analyzed and compared for a single site going through transformation as well as between two or more sites simultaneously tested.

I believe emergent properties of my system exist. The relatively consistent patterns of successional changes to both forested and cleared sites are emergent because the level of complexity (from changes in species to changes in microclimate, to changes in soil characteristics) that drives succession seems impossible to break down to constituent parts. Another emergent aspect of this system are the synergistic effects of mutualistic relationships, for example: mycorrhizal partnerships, pollination, rhizobium–plant connections, and lichens. Like the successional changes mentioned above, the synergistic effects of these relationships cannot be broken into constituent parts. In fact, it seems that these mutualistic relationships are often the involved with driving successional changes.
Ecosystems are incredibly complex in their functioning. As humans, we can create models to help us see important connections between seemingly disparate parts of the whole. We can make models that are heirachical, showing different scale perspectives on an ecosystem as well as how elements from different hierarchical levels are impacting one another. We can also create models that show cause and effect chains, eventually recognizing that there are feedback loops along these complex chains that were not easy to take into account when merely looking at single components of an ecosystem. Perhaps, most importantly, we need to remember the fallacies we tend to fall into when we create understanding of complex systems.
Inevitably the limitations of our own observational skills, logical errors made when designing experiments, tendency to misinterpret what our results mean, limited size of our knowledge base, and bloated egos will cause us to fall far short of complete understanding of such systems, with their thousands or millions of interacting parts and processes…but, lets not let that stand in our way of trying. We’ve come a long way.