Integrating wildlife count models with state-and-transitions models to enhance rangeland management for multiple objectives

Authors: TIMMER, J - Colorado State University; TIPTON, C - Colorad0 State University; BRUEGGER, R - Colorado State University; Augustine, David; DICKEY, C - Colorado State University; FERNANDEZ-GIMENEZ, MARIA - Colorado State University; ALDRIDGE, C - Colorado State University

Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/19/2021
Publication Date: 6/10/2021
Citation: Timmer, J.M., Tipton, C.Y., Bruegger, R.A., Augustine, D.J., Dickey, C., Fernandez-Gimenez, M., Aldridge, C. 2021. Integrating wildlife count models with state-and-transitions models to enhance rangeland management for multiple objectives. Rangeland Ecology and Management. 78:15-25. https://doi.org/10.1016/j.rama.2021.04.005.
DOI: https://doi.org/10.1016/j.rama.2021.04.005

Interpretive Summary: State-and-transition models (STM) are a tool used in rangeland management to describe changes in vegetation over time. STMs can be improved by addressing wildlife habitat in addition to vegetation, so land managers can predict how wildlife populations might change in response to management of livestock grazing. Our objective was to show how densities of several bird species are related to a STM for sagebrush rangelands in northwest Colorado to guide local management. The STM included two shrub-dominated community phases, a native grassland state, and an exotic-dominated state divided into a shrubland and grassland phase. We surveyed randomly distributed plots for songbirds and greater sage-grouse (Centrocercus urophasianus) pellets, collected a suite of vegetation indicators at each plot, and quantified bird habitat relationships with count-based regression models. We then used the habitat models to predict how songbird and sage-grouse density are expected to vary among and within community states and phases. Moderate or increasing shrub cover were important predictors for shrub-associated species, and responses to understory components varied by species. In the STM, we predicted higher densities of shrub-associated bird species in the shrub-dominated phases and higher densities for grassland-associated bird species in the state and phase lacking shrub cover. However, no single state or phase captured the highest density for all bird species illustrating the value of having multiple vegetation types dispersed across the landscape. Our results also illustrate the value in comparing quantitative wildlife habitat models with the range of vegetation conditions associated with each STM state or phase, which shows how bird density can change within states and phases. Our approach can assist local land managers and landowners to gauge the potential impacts of land-use decisions on bird populations, especially for species of conservation concern.

Technical Abstract: State-and-transition models (STM) are a tool used in rangeland management to describe linear and non-linear vegetation dynamics as conceptual models. STMs can be improved by addressing multiple ecosystem services, such as wildlife habitat, so land managers can predict how wildlife populations might change in response to drivers of change, and by illustrating the tradeoffs in managing for different ecosystem services. Our objective was to incorporate avifauna density into a collaboratively developed STM for sagebrush rangelands in northwest Colorado to guide local management of sagebrush avifauna. The STM included two shrub-dominated community phases, a native grassland state, and an exotic-dominated state divided into a shrubland and grassland phase. We surveyed randomly distributed plots for songbirds and greater sage-grouse (Centrocercus urophasianus) pellets, collected a suite of vegetation indicators at each plot, and quantified avifauna habitat relationships with count-based regression models. We then used the habitat models to predict indicies of songbird and sage-grouse density based on average vegetation values per state or community phase. Moderate or increasing shrub cover were important predictors for shrub-associated species, and responses to understory components varied by species. In the STM, we predicted higher densities of shrub-associated bird species in the shrub-dominated phases and higher densities for grassland-associated bird species in the state and phase lacking shrub cover. However, no single state or phase captured the highest density for all bird species illustrating the value of landscape heterogeneity. Our results also illustrate the value in comparing quantitative wildlife habitat models with the range of vegetation conditions associated with each STM state or phase to understand how bird density can change within states and phases. Our approach can assist local land managers and landowners to gauge the potential impacts of land-use decisions on avifauna populations, especially for species of conservation concern.