Detecting failure of climate predictions

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055

Robust assessment of associations between weather and eastern wild turkey nest success

Temperature and precipitation have been identified as factors that potentially influence eastern wild turkey (Meleagris gallopavo silvestris) reproduction, but robust analyses testing the relationship between weather parameters and turkey nest success are lacking. Therefore, we assessed how weather influenced turkey daily nest survival using 8 years of data collected from 715 nests across the southeastern United States. We also conducted exploratory analyses investigating if weather conditions during or prior to nesting best predicted nest success. We then assessed the possible implications of climate change through 2041–2060 for future eastern wild turkey daily nest survival and nest success for variables determined significant in analyses. During incubation, positive anomalies of minimum daily temperature were associated with greater daily nest survival. Precipitation during nesting was not a good predictor of daily nest survival. Exploratory analyses unexpectedly indicated that weather conditions in January prior to incubation were more important to nest success than weather conditions during incubation. In January, negative anomalies of minimum temperature and greater average daily precipitation were associated with greater nest success. Projections of future nest success or daily nest survival based on these relationships with the predictive covariates, and informed by climate models, suggest that nest success may increase as January precipitation increases and that daily nest survival may increase as temperature during incubation increases. These positive associations could be offset by a negative association between nest success and the expected increases in January minimum average temperature. Additional research is needed to investigate causes of these relationships and assess the implications of climate change for eastern wild turkey poult survival

Minimal shift of eastern wild turkey nesting phenology associated with projected climate change

Climate change may induce mismatches between wildlife reproductive phenology and temporal occurrence of resources necessary for reproductive success. Verifying and elucidating the causal mechanisms behind potential mismatches requires large-scale, longer-duration data. We used eastern wild turkey (Meleagris gallopavo silvestris) nesting data collected across the southeastern U.S. over eight years to investigate potential climatic drivers of variation in nest initiation dates. We investigated climactic relationships with two datasets, one inclusive of successful and unsuccessful nests (full dataset) and another of just successful nests (successfully hatched dataset), to determine whether successfully hatched nests responded differently to weather changes than all nests did. In the full dataset, each 10 cm increase in January precipitation was associated with nesting occurring 0.46–0.66 days earlier, and each 10 cm increase in precipitation during the 30 days preceding nesting was associated with nesting occurring 0.17–0.21 days later. In the successfully hatched dataset, a 10 cm increase in March precipitation was associated with nesting occurring 0.67–0.74 days earlier, and an increase of one unit of variation in February maximum temperature was associated with nesting occurring 0.02 days later. We combined the results of these modeled relationships with multiple climate scenarios to understand potential implications of future climate change on wild turkey nesting phenology; results indicated that mean nest initiation date is projected to change by \u3c0.1 day by 2040–2060. Wild turkey nesting phenology did not track changes in spring green-up timing, which could result in phenological mismatch between the timing of nesting and the availability of resources critical for successful reproduction

Developing a translational ecology workforce

We define a translational ecologist as a professional ecologist with diverse disciplinary expertise and skill sets, as well as a suitable personal disposition, who engages across social, professional, and disciplinary boundaries to partner with decision makers to achieve practical environmental solutions. Becoming a translational ecologist requires specific attention to obtaining critical non-scientific disciplinary breadth and skills that are not typically gained through graduate-level education. Here, we outline a need for individuals with broad training in interdisciplinary skills, use our personal experiences as a basis for assessing the types of interdisciplinary skills that would benefit potential translational ecologists, and present steps that interested ecologists may take toward becoming translational. Skills relevant to translational ecologists may be garnered through personal experiences, informal training, short courses, fellowships, and graduate programs, among others. We argue that a translational ecology workforce is needed to bridge the gap between science and natural resource decisions. Furthermore, we argue that this task is a cooperative responsibility of individuals interested in pursuing these careers, educational institutions interested in training scientists for professional roles outside of academia, and employers seeking to hire skilled workers who can foster stakeholder-engaged decision making

Robust Projections of Future Fire Probability for the Conterminous United States

Globally increasing wildfires have been attributed to anthropogenic climate change. However, providing decision makers with a clear understanding of how future planetary warming could affect fire regimes is complicated by confounding land use factors that influence wildfire and by uncertainty associated with model simulations of climate change. We use an ensemble of statistically downscaled Global Climate Models in combination with the Physical Chemistry Fire Frequency Model (PC2FM) to project changing potential fire probabilities in the conterminous United States for two scenarios representing lower (RCP 4.5) and higher (RCP 8.5) greenhouse gas emission futures. PC2FM is a physically-based and scale-independent model that predicts mean fire return intervals from both fire reactant and reaction variables, which are largely dependent on a locale\u27s climate. Our results overwhelmingly depict increasing potential fire probabilities across the conterminous US for both climate scenarios. The primary mechanism for the projected increases is rising temperatures, reflecting changes in the chemical reaction environment commensurate with enhanced photosynthetic rates and available thermal molecular energy. Existing high risk areas, such as the Cascade Range and the Coastal California Mountains, are projected to experience greater annual fire occurrence probabilities, with relative increases of 122% and 67%, respectively, under RCP 8.5 compared to increases of 63% and 38% under RCP 4.5. Regions not currently associated with frequently occurring wildfires, such as New England and the Great Lakes, are projected to experience a doubling of occurrence probabilities by 2100 under RCP 8.5. This high resolution, continental-scale modeling study of climate change impacts on potential fire probability accounts for shifting background environmental conditions across regions that will interact with topographic drivers to significantly alter future fire probabilities. The ensemble modeling approach presents a useful planning tool for mitigation and adaptation strategies in regions of increasing wildfire risk

Foundations of Translational Ecology

Ecologists who specialize in translational ecology (TE) seek to link ecological knowledge to decision making by integrating ecological science with the full complement of social dimensions that underlie today\u27s complex environmental issues. TE is motivated by a search for outcomes that directly serve the needs of natural resource managers and decision makers. This objective distinguishes it from both basic and applied ecological research and, as a practice, it deliberately extends research beyond theory or opportunistic applications. TE is uniquely positioned to address complex issues through interdisciplinary team approaches and integrated scientist–practitioner partnerships. The creativity and context-specific knowledge of resource managers, practitioners, and decision makers inform and enrich the scientific process and help shape use-driven, actionable science. Moreover, addressing research questions that arise from on-the-ground management issues – as opposed to the top-down or expert-oriented perspectives of traditional science – can foster the high levels of trust and commitment that are critical for long-term, sustained engagement between partners

Combined Tumor Cell-Based Vaccination and Interleukin-12 Gene Therapy Polarizes the Tumor Microenvironment in Mice

Tumor progression depends on tumor milieu, which influences neovasculature formation and immunosuppression. Combining immunotherapy with antiangiogenic/antivascular therapy might be an effective therapeutic approach. The aim of our study was to elaborate an anticancer therapeutic strategy based on the induction of immune response which leads to polarization of tumor milieu. To achieve this, we developed a tumor cell-based vaccine. CAMEL peptide was used as a B16-F10 cell death-inducing agent. The lysates were used as a vaccine to immunize mice bearing B16-F10 melanoma tumors. To further improve the therapeutic effect of the vaccine, we combined it with interleukin (IL)-12 gene therapy. IL-12, a cytokine with antiangiogenic properties, activates nonspecific and specific immune responses. We observed that combined therapy is significantly more effective (as compared with monotherapies) in inhibiting tumor growth. Furthermore, the tested combination polarizes the tumor microenvironment, which results in a switch from a proangiogenic/immunosuppressive to an antiangiogenic/immunostimulatory one. The switch manifests itself as a decreased number of tumor blood vessels, increased levels of tumor-infiltrating CD4+, CD8+ and NK cells, as well as lower level of suppressor lymphocytes (Treg). Our results suggest that polarizing tumor milieu by such combined therapy does inhibit tumor growth and seems to be a promising therapeutic strategy