Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 24, 2014
Publication Date: N/A
Interpretive Summary: Rangeland management is largely focused on vegetation and factors which influence vegetation change. Standard methods for categorizing vegetation involve describing possible plant communities on a given site and listing factors which might cause transitions from one plant community to another. Management efforts are usually intended to keep desirable communities from transitioning to those which are weed-dominated or in some other way fail to meet management objectives. In most cases, current approaches do not effectively capture the mechanisms which cause a community to transition from desirable to undesirable. The tendency is to capture changes that have occurred in the past and make assumptions about the causes of the change. We propose a framework which highlights plant mortality and recruitment (death loss and establishment of seedlings) as the mechanisms for plant community change, and argue for the importance of perennial bunchgrasses in maintaining the desirability of many rangeland plant communities. Adoption of this framework would help coordinate a broad array of research and management activities and would provide a means of evaluating resilience and potentially the health of rangelands.
Technical Abstract: Rangeland management is largely focused on managing vegetation change. Objectives may include managing against change if the desired vegetation is in place, or attempting to create a shift in vegetation if the desired state is not present. Describing potential vegetation states and requirements for maintaining or shifting the states is a critical for decision-making. State-and-transition models (STMs) have become the method of choice for describing vegetation dynamics on rangelands. One shortcoming of the STM approach is that it largely relies on observations of past change, and as currently applied tends to have limited predictive capability. We propose using the regeneration niche concept to improve the predictive capability of STMs. In the simple terms, the concept simply states that as plants die and leave gaps in the community, recruitment of new individuals will dictate successional direction. If existing species fill the gaps, resilience is implied. If new species fill the gaps, then at some point a state change will occur. Existing literature shows that most rangeland bunchgrass have average life spans of 10 years or less, so periodic recruitment is necessary to maintain communities even if there is no mortality from disturbance. We propose that rangelands should be evaluated in the context of plant mortality and recruitment, and that most rangelands fall in one of three disturbance categories: 1) disturbance is minimal and mortality is based on species life spans, 2) chronic disturbance influences life spans and recruitment, 3) acute disturbance causes mortality over short time periods and influences recruitment dynamics. We also suggest that more emphasis be placed on the concept of critical transitions and less on the degree of disturbance. In other words, a small disturbance at the wrong point in community composition (low plant density and high gap size for example) can cause a transition, whereas major disturbance in a high condition community may yield little risk of transition. These concepts are consistent with previous literature on resilience and "at-risk" communities, but have not received attention from the standpoint of mortality and recruitment. Predicting changes in successional trajectories requires an understanding of life histories, limitations to recruitment, and interactions with weather patterns. In many cases, specific sequences of events are required to trigger recruitment. The proposed approach is consistent with previously published concepts on states and transitions and would provide a means of organizing diverse research results into a framework managers can use to make decisions, and researchers can use to prioritize research needs.