arXiv subjects

Malcolm S. Itter

Publications and source records attributed to Malcolm S. Itter.

4 recordsLinked to original sources

arXiv2025

Multivariate spatial models for small area estimation of species-specific forest inventory parameters

National Forest Inventories (NFIs) provide statistically reliable information on forest resources at national and other large spatial scales. As forest management and conservation needs become increasingly complex, NFIs are being called upon to provide forest parameter estimates at spatial scales smaller than current design-based estimation procedures can provide. This is particularly true when estimates are desired by species or species groups. Here we propose a multivariate spatial model for small area estimation of species-specific forest inventory parameters. The hierarchical Bayesian modeling framework accounts for key complexities in species-specific forest inventory data, such as zero-inflation, correlations among species, and residual spatial autocorrelation. Importantly, by fitting the model directly to the individual plot-level data, the framework enables estimates of species-level forest parameters, with associated uncertainty, across any user-defined small area of interest. A simulation study revealed minimal bias and higher accuracy of the proposed model-based approach compared to design-based estimator. We applied the model to estimate species-specific county-level aboveground biomass for the 20 most abundant tree species in the southern United States using Forest Inventory and Analysis (FIA) data. Model-based biomass estimates had high correlations with design-based estimates, yet the model-based estimates tended to have a slight positive bias relative to design-based estimates. Importantly, the proposed model provided large gains in precision across all 20 species. On average across species, 91.5% of county-level biomass estimates had higher precision compared to the design-based estimates. The proposed framework improves the ability of NFI data users to generate species-level forest parameter estimates with reasonable precision at management-relevant spatial scales.

stat.AP↗

arXiv2019

EcoMem: An R package for quantifying ecological memory

Ecological processes may exhibit memory to past disturbances affecting the resilience of ecosystems to future disturbance. Understanding the role of ecological memory in shaping ecosystem responses to disturbance under global change is a critical step toward developing effective adaptive management strategies to maintain ecosystem function and biodiversity. We developed EcoMem, an R package for quantifying ecological memory functions using common environmental time series data (continuous, count, proportional) applying a Bayesian hierarchical framework. The package estimates memory functions for continuous and binary (e.g., disturbance chronology) variables making no a priori assumption on the form of the functions. EcoMem allows users to quantify ecological memory for a wide range of ecosystem processes and responses. The utility of the package to advance understanding of the memory of ecosystems to environmental drivers is demonstrated using a simulated dataset and a case study assessing the memory of boreal tree growth to insect defoliation.

stat.CO↗

arXiv2016

A Model-Based Approach to Wildland Fire Reconstruction Using Sediment Charcoal Records

Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate probability of fire associated with charcoal counts from individual-lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100-350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleo-fire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.

stat.AP↗

arXiv2016

Variable Effects of Climate on Forest Growth in Relation to Climate Extremes, Disturbance, and Forest Stand Dynamics

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health are complicated by potential interactions with disturbance events and stand dynamics. The effects of stand dynamics on forest responses to climate and disturbance are particularly important given forest characteristics driven by stand dynamics can be modified through forest management with the goal of increasing forest resistance and resilience to climate change. We develop a hierarchical Bayesian state-space model allowing climate effects on tree growth to vary over time and in relation to climate extremes, disturbance events, and stand dynamics. We apply the model to a dendrochronology dataset comprising measurements from forest stands of varying composition, structure, and development stage in northeastern Minnesota. Results indicate average forest growth was most sensitive to variables describing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with forest tent caterpillar defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances or insect defoliation events. Forest growth was most sensitive to water deficit during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest that forest growth resistance and resilience to interactions between climate extremes and insect defoliation can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands.

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