Quantifying long‐term plant community dynamics with movement models: implications for ecological resilience. Issue 5 (30th May 2017)

• Record Type: Journal Article
• Authors: Bagchi, Sumanta; Singh, Navinder J.; Briske, David D.; Bestelmeyer, Brandon T.; McClaran, Mitchel P.; Murthy, Karthik
• Is Part Of: Ecological applications. Volume 27:Issue 5(2017)
• Page Start: 1514
• Page End: 1528
• Publication Date: 2017-05-30
• Subjects: long‐term monitoring -- model selection -- multiple equilibria -- nonlinear regression -- parsimony -- resilience indicators -- time series -- tipping points -- vegetation change; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.1544
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: Quantification of rates and patterns of community dynamics is central for understanding the organization and function of ecosystems. These insights may support a greater empirical understanding of ecological resilience, and the application of resilience concepts toward ecosystem management. Distinct types of dynamics in natural communities can be used to interpret and apply resilience concepts, but quantitative methods that can systematically distinguish among them are needed. We develop a quantitative method to analyze long‐term records of plant community dynamics using principles of movement ecology. We analyzed dissimilarity of species composition through time with linear and nonlinear statistical models to assign community change to four classes of movement trajectories. Compositional change in each sampled plot through time was classified into four classes, stability, abrupt nonlinear change, transient reversible change, and gradual linear drift, each representing a different aspect of ecological resilience. These competing models were evaluated based on estimated coefficients, goodness of fit, and parsimony. We tested our method's accuracy and robustness through simulations, or the ability to distinguish among trajectories and classify them correctly. We simulated 16, 000 trajectories of four types, of which 94–100% were correctly classified. Next, we analyzed 13 long‐term vegetation records from North American grasslands (annual grasslands with warm‐season and cool‐season communities, shortgrass, mixedgrass, and tallgrass prairies, and sagebrush steppe), and a record of primary succession at Mt. St. Helens volcano. Collectively, we analyzed 14, 647 observations from 775 plots, between 1915 and 2012. Dynamics could be reliably assigned for 705 plots (91%), and overall statistical fit was high (goodness of fit, 0.77 ± 0.15 SD). Among the perennial grasslands, stability was most common (44% of all plots), followed by gradual linear (22%), abrupt nonlinear (17%), and reversible (6%) change. Among annual grasslands, abrupt nonlinear shifts (33%) were more common in the warm‐season community than in the cool‐season (20%). As expected, abrupt nonlinear change was common during primary succession (51%) while reversible change was rare (3%). Generally, reversible dynamics often required 2–3 decades. Analysis of long‐term community change, or trajectories, with principles of movement ecology provides a quantitative basis to compare and interpret ecological resilience within and among ecosystems.