The most widespread forest type in the basin, is characterized as a dynamic system (high productivity and dispersal), and a historically short fire rotation.
Applications
Applying food web metrics
Metrics that quantify entire communities can provide useful information to track, manage and promote a system’s health, and its ability to be resistant in the face of disturbance and threats associated with climate change.
Two main forest types in the Lake Tahoe basin are Sierran mixed conifer (hereafter mixed conifer) and red fir forests. As climate warms, it is predicted that red fir forests will decline and likely transition to mixed conifer. Constructing food webs of these two forest wildlife communities that we know well has two goals: (1) validate our metrics, do metrics match our understanding of the system? Are we able to create metrics that measure the system holistically? (2) Determine if metrics are similar or different, doing so will provide insights into what may be into what structure and function may be lost.
How does the composition, structure, and funnction of mixed conifer and red fir forest wildlife communities differ?
Community composition
Species (Fig 1A) and guild richness (Fig 1B - functional groups) are higher in mixed conifer forests compared to red fir forests. This matches our knowledge of the systems, and, just because red fir forest is less biodiverse (lower species & guild richness), doesn’t mean it’s less valuable, they are different.
The two forest types occur in different life zones and support some different species. For example, Clark’s nutcrackers are important seed dispersers of conifers, and they are found in higher elevation red fir forests. While Mixed conifer forests are home to striped and spotted skunks, who are mesocarnivores, which can be important in maintaining food web stability.
Community structure and function
Functional similarity is generally alike in the two forests (Figure 2).
This suggests that multiple species may perform similar functions and that this redundancy may allow communities to be more resilient in the face of random species loss.
While similar, on average maximum chain length (calculated as the mean guild maximum chain length (mcl)) is higher in mixed conifer compared to red fir forest (Figure 3).
In general longer chain lengths are associated with less stable systems, because the loss of a top (apex) predator can have cascading impacts on the rest of the community.
Modularity is similar and generally high for both forest types (Figure 4).
High modularity (a high number of subcommunities) can be good because it means that if a random species is lost, then their loss will be less likely to have cascading impacts on the rest of the community. However, high modularity can also occur when there has been a simplification of the system - such as the loss of top predators. This is the case for the Central Sierra, where predators including grizzly bears and wolverines went extinct when westward expansion by pioneers occurred in the late 1800s and early 1900s.
Overall these metrics suggest that the wildlife communities in mixed conifer and fed fir forests differ in their composition, but are similar in structure which suggests that they will respond similarly to the loss of random species or top predators. But this is just the start, here we discussed resilience to species loss, but what about to other potential stressors (fire, drought, pests, disease, etc.). Further, using these metrics we can explore questions that address both basic ecological questions (e.g., what are the factors that drive community assembly, and how will they change into the future?) and applied questions (e.g., do wildlife communities differ in their resilience to different management strategies?), both of which will help us to inform management and policy makers to help promote healthy and resilient forests.
Future applications
Forest composition: With variation in management strategies and climate change, forest composition is expected to change into the future. Identifying patterns in forest composition (e.g., vegetation type) and associated wildlife communities will be an important tool for managers to understand how to support diverse communities into the future.
Structure: Forest structure is important for fire management. Understanding patterns in forest structure and how that influences communities will be important for communicating to managers how best to manage for dynamic landscapes that also support biodiversity.
Landscape management: Evaluating how communities are impacted by different types of management will be critical to understand how decisions now can support resilient wildlife communities into the future. Further, performing this work on large spatial scales (e.g., the aforementioned TCSI) will help us understand how management influences wildlife communities across larger landscapes.