An integration framework for linking avifauna niche and forest landscape models.

Avian cavity nesters (ACN) are viable indicators of forest structure, composition, and diversity. Utilizing these species responses in multi-disciplinary climate-avian-forest modeling can improve climate adaptive management. We propose a framework for integrating and evaluating climate-avian-forest models by linking two ACN niche models with a forest landscape model (FLM), LANDIS-II. The framework facilitates the selection of available ACN models for integration, evaluation of model transferability, and evaluation of successful integration of ACN models with a FLM. We found selecting a model for integration depended on its transferability to the study area (Northern Rockies Ecoregion of Idaho in the United States), which limited the species and model types available for transfer. However, transfer evaluation of the tested ACN models indicated a good fit for the study area. Several niche model variables (canopy cover, snag density, and forest cover type) were not directly informed by the LANDIS-II model, which required secondary modeling (Random Forest) to derive values from the FLM outputs. In instances where the Random Forest models performed with a moderate classification accuracy, the overall effect on niche predictions was negligible. Predictions based on LANDIS-II simulations performed similarly to predictions based on the niche model's original training input types. This supported the conclusion that the proposed framework is viable for informing avian niche models with FLM simulations. Even models that poorly approximate habitat suitability, due to the inherent constraints of predicting spatial niche use of irruptive species produced informative results by identifying areas of management focus. This is primarily because LANDIS-II estimates spatially explicit variables that were unavailable over large spatial extents from alternative datasets. Thus, without integration, one of the ACN niche models was not applicable to the study area. The framework will be useful for integrating avifauna niche and forest ecosystem models, which can inform management of contemporary and future landscapes under differing management and climate scenarios.

Climate change, woodpeckers, and forests: Current trends and future modeling needs.

The structure and composition of forest ecosystems are expected to shift with climate-induced changes in precipitation, temperature, fire, carbon mitigation strategies, and biological disturbance. These factors are likely to have biodiversity implications. However, climate-driven forest ecosystem models used to predict changes to forest structure and composition are not coupled to models used to predict changes to biodiversity. We proposed integrating woodpecker response (biodiversity indicator) with forest ecosystem models. Woodpeckers are a good indicator species of forest ecosystem dynamics, because they are ecologically constrained by landscape-scale forest components, such as composition, structure, disturbance regimes, and management activities. In addition, they are correlated with forest avifauna community diversity. In this study, we explore integrating woodpecker and forest ecosystem climate models. We review climate-woodpecker models and compare the predicted responses to observed climate-induced changes. We identify inconsistencies between observed and predicted responses, explore the modeling causes, and identify the models pertinent to integration that address the inconsistencies. We found that predictions in the short term are not in agreement with observed trends for 7 of 15 evaluated species. Because niche constraints associated with woodpeckers are a result of complex interactions between climate, vegetation, and disturbance, we hypothesize that the lack of adequate representation of these processes in the current broad-scale climate-woodpecker models results in model-data mismatch. As a first step toward improvement, we suggest a conceptual model of climate-woodpecker-forest modeling for integration. The integration model provides climate-driven forest ecosystem modeling with a measure of biodiversity while retaining the feedback between climate and vegetation in woodpecker climate change modeling.

A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system.

Modeling the magnitude and distribution of sediment-bound pollutants in estuaries is often limited by incomplete knowledge of the site and inadequate sample density. To address these modeling limitations, a decision-support tool framework was conceived that predicts sediment contamination from the sub-estuary to broader estuary extent. For this study, a Random Forest (RF) model was implemented to predict the distribution of a model contaminant, triclosan (5-chloro-2-(2,4-dichlorophenoxy)phenol) (TCS), in Narragansett Bay, Rhode Island, USA. TCS is an unregulated contaminant used in many personal care products. The RF explanatory variables were associated with TCS transport and fate (proxies) and direct and indirect environmental entry. The continuous RF TCS concentration predictions were discretized into three levels of contamination (low, medium, and high) for three different quantile thresholds. The RF model explained 63% of the variance with a minimum number of variables. Total organic carbon (TOC) (transport and fate proxy) was a strong predictor of TCS contamination causing a mean squared error increase of 59% when compared to permutations of randomized values of TOC. Additionally, combined sewer overflow discharge (environmental entry) and sand (transport and fate proxy) were strong predictors. The discretization models identified a TCS area of greatest concern in the northern reach of Narragansett Bay (Providence River sub-estuary), which was validated with independent test samples. This decision-support tool performed well at the sub-estuary extent and provided the means to identify areas of concern and prioritize bay-wide sampling.

Use of intravenous insulin aspart for treatment of naturally occurring diabetic ketoacidosis in dogs.

OBJECTIVES: To characterize the utility and safety of IV insulin aspart in the treatment of diabetes ketoacidosis (DKA) in dogs and to determine the times to resolution of hyperglycemia, ketonemia, and acidemia in dogs treated with IV insulin aspart. DESIGN: Prospective noncontrolled single arm study of dogs with DKA between February 2010 and March 2011. SETTING: University teaching hospital. ANIMALS: Six dogs with spontaneous DKA and blood glucose (BG) concentration >13.8 mmol/L (250 mg/dL), pH between 7.0 and 7.35, and blood beta-hydroxybutyrate >2.0 mmol/L were treated with an IV continuous rate infusion (CRI) of aspart insulin. The time to biochemical resolution of DKA was defined as the time interval from when the IV CRI of aspart insulin began until marked hyperglycemia (BG concentration >13.8 mmol/L [250 mg/dL]), acidemia (venous pH <7.35), and ketonemia (beta-hydroxybutyrate concentration >2.0 mmol/L) resolved. Aspart insulin was administered as an IV CRI at an initial dose of 0.09 U/kg/h. The dose was adjusted according to a previously published protocol. MEASUREMENTS AND MAIN RESULTS: The median time to biochemical resolution of DKA in dogs treated with insulin aspart was 28 hours (range, 20-116 h). Mean BG concentration decreased significantly from the time IV fluid resuscitation began (32.0 mmol/L [576 mg/dL]; range, 14.9-38.9 mmol/L [268-700 mg/dL]) until 6 hours later when IV aspart insulin CRI began (20.1 mmol/L [363 mg/dL]; range, 9.4-26.1 mmol/L [169-470 mg/dL], P = 0.03). No adverse effects were observed in association with IV insulin aspart administration. Median cost of hospitalization was US$3,477 (range, US$1,483-10,469). Median total units per kilogram of administered IV insulin aspart was 2.97 U/kg (range, 2.04-10.52 U/kg). CONCLUSIONS: Intravenous CRI of insulin aspart is a safe and effective treatment for DKA in dogs. IV fluid resuscitation is recommended prior to insulin administration.

Pulmonary surfactant proteins A and D directly suppress CD3+/CD4+ cell function: evidence for two shared mechanisms.

Pulmonary surfactant is a lipoprotein complex that lowers surface tension at the air-liquid interface of the lung and participates in pulmonary host defense. Surfactant proteins (SP), SP-A and SP-D, modulate a variety of immune cell functions, including the production of cytokines and free radicals. Previous studies showed that SP-A and SP-D inhibit lymphocyte proliferation in the presence of accessory cells. The goal of this study was to determine whether SP-A and SP-D directly suppress Th cell function. Both proteins inhibited CD3(+)/CD4(+) lymphocyte proliferation induced by PMA and ionomycin in an IL-2-independent manner. Both proteins decreased the number of cells entering the S and mitotic phases of the cell cycle. Neither SP-A nor SP-D altered cell viability, apoptosis, or secretion of IL-2, IL-4, or IFN-gamma when Th cells were treated with PMA and ionomycin. However, both proteins attenuated ionomycin-induced cytosolic free calcium ([Ca(2+) ](i)), but not thapsigargin-induced changes in [Ca(2+)](i). In summary, inhibition of T cell proliferation by SP-A and SP-D occurs via two mechanisms, an IL-2-dependent mechanism observed with accessory cell-dependent T cell mitogens and specific Ag, as well as an IL-2-independent mechanism of suppression that potentially involves attenuation of [Ca(2+)](i).