Using structural equation modeling to link human activities to wetland ecological integrity

Abstract

Abstract. The integrity of wetlands is of global concern. A common approach to evaluating ecological integrity involves bioassessment procedures that quantify the degree to which communities deviate from historical norms. While helpful, bioassessment provides little information about how altered conditions connect to community response. More detailed information is needed for conservation and restoration. We have illustrated an approach to addressing this challenge using structural equation modeling (SEM) and long-term monitoring data from Rocky Mountain National Park (RMNP). Wetlands in RMNP are threatened by a complex history of anthropogenic disturbance including direct alteration of hydrologic regimes; elimination of elk, wolves, and grizzly bears; reintroduction of elk (absent their primary predators); and the extirpation of beaver. More recently, nonnative moose were introduced to the region and have expanded into the park. Bioassessment suggests that up to half of the park's wetlands are not in reference condition. We developed and evaluated a general hypothesis about how human alterations influence wetland integrity and then develop a specific model using RMNP wetlands. Bioassessment revealed three bioindicators that appear to be highly sensitive to human disturbance (HD): (1) conservatism, (2) degree of invasion, and (3) cover of native forbs. SEM analyses suggest several ways human activities have impacted wetland integrity and the landscape of RMNP. First, degradation is highest where the combined effects of all types of direct HD have been the greatest (i.e., there is a general, overall effect). Second, specific HDs appear to create a "mixed-bag" of complex indirect effects, including reduced invasion and increased conservatism, but also reduced native forb cover. Some of these effects are associated with alterations to hydrologic regimes, while others are associated with altered shrub production. Third, landscape features created by historical beaver activity continue to influence wetland integrity years after beavers have abandoned sites via persistent landforms and reduced biomass of tall shrubs. Our model provides a system-level perspective on wetland integrity and provides a context for future evaluations and investigations. It also suggests scientifically supported natural resource management strategies that can assist in the National Park Service mission of maintaining or, when indicated, restoring ecological integrity "unimpaired for future generations."

Glomalin amount and compositional variation, and their associations with soil properties in farmland, northeastern China

“…As a result, we may test the idea that variable C is influenced by the variable A through the mediating effect of B ( i.e ., A → B → C ). This flexibility in equational representation has numerous benefits, including the representation of complete hypotheses and the discovery of unanticipated relationships ( e.g ., effects of A on C not through B ) ( Schweiger et al, ). In this paper, SEM was used in identifying the direct and indirect effects from soil properties on GRSP amount and compositional traits.…”

Operationalizing Ecological Resilience Concepts for Managing Species and Ecosystems at Risk

“…In many cases, landscape boundaries are linked to management jurisdictions, such as parks or reserves (e.g., Schweiger et al, 2016). Landscapes defined by humans may or may not correspond with natural boundaries or spatial regimes (Sundstrom et al, 2017;Roberts et al, 2018).…”

Climate and plant controls on soil organic matter in coastal wetlands

“…In coastal and freshwater wetlands, vegetation-based indices can be used to characterize long-term abiotic conditions (e.g., hydrology, salinity) that are too expensive or logistically difficult to measure in a large number of sites (Schweiger, Grace, Cooper, Bobowski, & Britten, 2016;Tiner, 1991;Visser, Sasser, Chabreck, & Linscombe, 1998 Krauss, Osland, Reef, and Ball (2016). We then used the species' salinity scores (Supporting Information Table S1) and the species-specific cover data to calculate a proxy index for plot salinity (i.e., for each plot, the species-specific cover data were multiplied by the species' salinity scores).…”