Monique E. Rocca

Ensemble habitat mapping of invasive plant species.

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis.

Indicators of ecosystem function identify alternate states in the sagebrush steppe

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.

Lodgepole Pine Seed Germination Following Tree Death from Mountain Pine Beetle Attack in Colorado, USA

Abstract Cones of lodgepole pine (Pinus contorta var. latifolia) are often serotinous, releasing their seeds from closed cones under heat from fire. Stand-replacing fires in predominantly serotinous stands can thus be expected to result in a strong regeneration response. After large-scale mortality caused by mountain pine beetle (Dendroctonus ponderosae), however, the seeds in serotinous cones may remain on the dead trees for a number of years, impacting germination and viability. We tested seeds collected from living and beetle-killed serotinous stands to determine whether they remain viable after tree death, and whether germination rates were affected by cone age. There was no significant difference in percent germination from the living stand vs. the dead stand. While there was a significant relationship between cone age and percent germination, cones that were 21–25 y still had >30% germination. We conclude that post-beetle regeneration likely will not be limited by viable seed availability in stands with serotinous cone-bearing trees.

Differences in Plant Species Composition as Evidence of Alternate States in the Sagebrush Steppe

Abstract State-and-transition models (STMs), conceptual models of vegetation change based on alternate state theory, are increasingly applied as tools for land management decision-making. As STMs are created throughout the United States, it is crucial to ensure that they are supported by ecological evidence. Plant species composition reflects ecosystem processes that are difficult to measure and may be a useful indicator of alternate states. This study aims to create data-driven STMs based on plant species composition for two ecological sites (Claypan and Mountain Loam) in northwestern Colorado sagebrush steppe. We sampled 76 plots with different management and disturbance histories. Drawing on the hierarchical approach currently taken to build STMs, we hypothesized that A) differences in species composition between the two ecological sites would be related to environmental factors and B) differences in species composition within each ecological site would be related to management and disturbance history. Relationships among species composition, site history, and environmental variables were evaluated using multivariate statistics. We found that between ecological sites, species composition was related to differences in soil texture, supporting Hypothesis A and the creation of separate STMs for each site. Within ecological sites, species composition was related to site history and also to environmental variation. This finding partially supports Hypothesis B and the identification of alternate states using species composition, but also suggests that these ecological sites are not uniform physical templates upon which plant community dynamics play out. This data-driven, plant species–based approach created two objective, credible STMs with states and transitions that are consistent with the sagebrush steppe literature. Our findings support the hierarchical view of landscapes currently applied in building STMs. An approach that acknowledges environmental heterogeneity within ecological sites is necessary to help define finer-resolution ecological sites and elucidate cases in which specific abiotic conditions make transitions between states more likely. Resumen Los Modelos de Estado y Transición (MET), que son modelos conceptuales en cambios de la vegetación basados la teoría del estado alternativo, su aplicación está en aumento como herramienta para la tomada de decisiones en el manejo de la tierra. Como los MET se han creado a través de los Estados Unidos, es vital que aseguremos que estos están apoyados por evidencia ecológica. La composición de especies refleja el proceso del ecosistema que es difícil de medir y podría ser un indicador útil de estados alternativos. Este estudio ayuda a crear un MET dirigido por datos basado en las composición de especies de plantas de dos sitios ecológicos (Claypan y Mountain Loam) en la estepa de artemisa al noroeste de de Colorado. Muestramos 76 parcelas con diferente manejo e historias de disturbio. Dibujando el concepto jerárquico actualmente usado para construir los MET, establecimos las siguientes hipótesis A) Las diferencias en la composición de especies entre los dos sitios ecológicos podrían estar relacionadas a factores medioambientales y B) las diferencias en la composición de especies dentro de cada sitio ecológicos podrían estar relacionadas por el manejo y la historia de disturbio. Las relaciones entre la composición de especies, la historia del sitio y las variables medioambientales fueron evaluadas usando estadística multivariada. Encontramos que entre sitios ecológicos, la composición de especies estuvo relacionada con las diferentes texturas del suelo, apoyando la Hipotesis A y la creación de MET separados. Dentro de los sitios ecológicos, la composición de especies estuvo relacionada a la historia del sitio y también a variables medioambientales. Estos resultados apoyan parcialmente la Hipotesis B y la identificación de estados alternativos usando la composición de especies, pero también sugieren que estos sitios ecológicos no son uniformes en la plantilla física que es donde la dinámica de la comunidad vegetal se desenvuelve. Este concepto basado en datos dirigidos en especies de plantas creo dos objetivos, creíbles MET con estados y transiciones que son consistentes con la literatura de la estepa de artemisa. Nuestros resultados apoyan el punto de vista jerárquico de paisajes que se usan actualmente para construir METs. Un enfoque que reconoce la heterogeneidad medioambiental dentro de sitios ecológicos es necesaria para ayudar a definir mejor resolución de sitios ecológicos y aclarar casos donde condiciones abióticas especificas hacen la transición mas probable.

Pacific-Atlantic Ocean influence on wildfires in northeast China (1774 to 2010)

National Natural Science Foundation of China [30970481, 41471168]; China National Key Research and Development Program [2016YFA0600800]; Program for Changjiang Scholars and Innovative Research Team in University [IRT15R09]; China Scholarship Council; Rocky Mountain Tree-Ring Research, Inc.; National Science Foundation (NSF) Accelerating Innovation Research (AIR) program; NSF Hazard SEES project