Alix I. Gitelman

CARRYOVER AQUATIC EFFECTS ON SURVIVAL OF METAMORPHIC FROGS DURING POND EMIGRATION

In organisms with complex life cycles, physiological stressors during early life stages may have fitness-level impacts that are delayed into later stages or habitats. We tested the hypothesis that body size and date of metamorphosis, which are highly responsive to aquatic stressors, influence post-metamorphic survival and movement patterns in the terrestrial phase of an ephemeral pond-breeding frog by examining these traits in two populations of northern red-legged frogs (Rana aurora aurora). To increase variation of body size at metamorphosis, we manipulated food availability for 314 of 1045 uniquely marked tadpoles and estimated the probability that frogs survived and emigrated using concentric rings of drift fencing surrounding ponds and Bayesian capture-recapture modeling. The odds of surviving and emigrating from the ponds to the innermost drift fences, approximately 12 m, increased by factors of 2.20 (95% credibility intervals 1.39-4.23) and 2.54 (0.94-4.91) with each millimeter increase in snout-vent length and decreased by factors of 0.91 (0.85-0.96) and 0.89 (0.80-1.00) with each days delay in metamorphosis for the two ponds. The odds of surviving and moving to the next ring of fencing, 12 m to approximately 40 m from the ponds, increased by a factor of 1.20 (0.45-4.06) with each millimeter increase in size. Our results demonstrated that body size and timing of metamorphosis relate strongly to the performance of newly metamorphosed frogs during their initial transition into terrestrial habitat. Carryover effects of aquatic stressors that reduce size and delay metamorphosis may have population-level impacts that are not expressed until terrestrial stages. Since changes in both aquatic and terrestrial systems are implicated in many amphibian declines, quantifying both immediate and delayed effects of stressors on demographic rates is critical to sound management.

Use of Aliphatic n-Alkynes To Discriminate Soil Nitrification Activities of Ammonia-Oxidizing Thaumarchaea and Bacteria

ABSTRACT Ammonia (NH3)-oxidizing bacteria (AOB) and thaumarchaea (AOA) co-occupy most soils, yet no short-term growth-independent method exists to determine their relative contributions to nitrification in situ. Microbial monooxygenases differ in their vulnerability to inactivation by aliphatic n-alkynes, and we found that NH3 oxidation by the marine thaumarchaeon Nitrosopumilus maritimus was unaffected during a 24-h exposure to ≤20 μM concentrations of 1-alkynes C8 and C9. In contrast, NH3 oxidation by two AOB (Nitrosomonas europaea and Nitrosospira multiformis) was quickly and irreversibly inactivated by 1 μM C8 (octyne). Evidence that nitrification carried out by soilborne AOA was also insensitive to octyne was obtained. In incubations (21 or 28 days) of two different whole soils, both acetylene and octyne effectively prevented NH4 +-stimulated increases in AOB population densities, but octyne did not prevent increases in AOA population densities that were prevented by acetylene. Furthermore, octyne-resistant, NH4 +-stimulated net nitrification rates of 2 and 7 μg N/g soil/day persisted throughout the incubation of the two soils. Other evidence that octyne-resistant nitrification was due to AOA included (i) a positive correlation of octyne-resistant nitrification in soil slurries of cropped and noncropped soils with allylthiourea-resistant activity (100 μM) and (ii) the finding that the fraction of octyne-resistant nitrification in soil slurries correlated with the fraction of nitrification that recovered from irreversible acetylene inactivation in the presence of bacterial protein synthesis inhibitors and with the octyne-resistant fraction of NH4 +-saturated net nitrification measured in whole soils. Octyne can be useful in short-term assays to discriminate AOA and AOB contributions to soil nitrification.

USE OF A SMOOTHER TO FORECAST OCCURRENCE OF EPIPHYTIC LICHENS UNDER ALTERNATIVE FOREST MANAGEMENT PLANS

We used habitat models to forecast the frequency of occurrence of epiphytic lichen species in a forested landscape under two alternative plans: a literal application of standard prescriptions in the Northwest Forest Plan and a plan patterned in part after natural disturbance regimes. The plans were evaluated for the Blue River watershed in the Cascade Range of Oregon, USA. We used two model types: logistic regression and an ecological neighborhood model using a form of nonparametric regression (SpOcc; Species Occurrence Modeler). Both logistic regression and SpOcc successfully estimated the occurrence of the lichen Lobaria oregana in the current landscape, based on elevation and structural classes of forests. Structural classes were defined by combinations of (1) overstory retention of remnant trees, (2) young cohort age, and (3) stream position (upland vs. riparian). Lobaria oregana rapidly diminished in frequency in the landscape at elevations above 900-1000 m. Young, even-aged stands had little or no Lobaria oregana. Most of the other structural classes, however, were very similar in frequency of Lobaria. The differences in lichens between retention levels and between mature and old growth stands were very small com- pared to the differences with respect to elevation and clearcutting. Similar results, with varying importances of elevation and forest structure, were obtained for most of the 20 species with the strongest models. Many of these species were associated with old growth, and many were forecasted to be more frequent under either management plan than in the current landscape. This is a likely response to the reduction of even-aged management in this landscape. We recommend using SpOcc for flexible nonparametric fitting of species relationships to a multivariate habitat. SpOcc avoids the need to specify response functions and, because it is multiplicative rather than additive, automatically models responses to interactions among predictors.

Spatial Designs and Properties of Spatial Correlation: Effects on Covariance Estimation

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.

Effects of Transportation and Other Factors on Survival Estimates of Juvenile Salmonids in the Unimpounded Lower Columbia River

Abstract We estimated the survival of juvenile salmonids out-migrating through the lower Columbia River to the Pacific Ocean. We tested the null hypotheses that no association exists between survival and transportation type (including barge transportation and in-river migration with no transportation), release date, river flow, mean body weight, and tag type. During 2002-2004, spring-summer (SS) and fall Chinook salmon Oncorhynchus tshawytscha and steelhead O. mykiss were implanted with uniquely coded radio or acoustic transmitter tags and released during the early, middle, and late out-migration periods. A series of receiver lines were used to detect these fish and estimate survival. Estimated survival varied considerably with transportation type and release date for fall Chinook salmon and steelhead, but the results were dependent upon year. Estimated survival between the lowermost dam (Bonneville Dam) and the upper estuary (river kilometer 46) was relatively high for SS Chinook salmon and steelhead, wh...

The effects of taurolidine alone and in combination with doxorubicin or carboplatin in canine osteosarcoma in vitro.

BackgroundOsteosarcoma (OS) affects over 8000 dogs/year in the United States. The disease usually arises in the appendicular skeleton and metastasizes to the lung. Dogs with localized appendicular disease benefit from limb amputation and chemotherapy but most die within 6–12 months despite these treatments. Taurolidine, a derivative of taurine, has anti-tumor and anti-angiogenic effects against a variety of cancers. The following in vitro studies tested taurolidine as a candidate for adjuvant therapy for canine OS. Tests for p53 protein status and caspase activity were used to elucidate mechanisms of taurolidine-induced cell death.ResultsTaurolidine was cytotoxic to osteosarcoma cells and increased the toxicity of doxorubicin and carboplatin in vitro. Apoptosis was greatly induced in cells exposed to 125 μM taurolidine and less so in cells exposed to 250 μM taurolidine. Taurolidine cytotoxicity appeared caspase-dependent in one cell line; with apparent mutant p53 protein. This cell line was the most sensitive to single agent taurolidine treatment and had a taurolidine-dependent reduction in accumulated p53 protein suggesting taurolidine’s effects may depend on the functional status of p53 in canine OS.ConclusionTaurolidine’s cytotoxic effect appears dependent on cell specific factors which may be explained, in part, by the functional status of p53. Taurolidine initiates apoptosis in canine OS cells and this occurs to a greater extent at lower concentrations. Mechanisms of cell death induced by higher concentrations were not elucidated here. Taurolidine combined with doxorubicin or carboplatin can increase the toxicity of these chemotherapy drugs and warrants further investigation in dogs with osteosarcoma.

Estimating Causal Effects from Multilevel Group-Allocation Data.

In group-allocation studies for comparing behavioral, social, or educational interventions, subjects in the same group necessarily receive the same treatment, whereby a group and/or group-dynamic effect can confound the treatment effect. General counterfactual outcomes that depend on group characteristics, group membership, and treatment are developed to provide a structure for specifying causal effects of treatment in the multilevel setting. An average causal effect of treatment cannot be specified, however, without a simplifying assumption of group-membership invariance (i.e., no group-dynamic effect). Under group-membership invariance and ignorability assumptions, the average causal effect is then connected to estimable quantities of the hierarchical linear model (HLM). Furthermore, it is shown that the typical specification of the HLM involves conditional independence assumptions that actually preclude the group-dynamic effect.

Next Generation of Ecological Indicators of Wetland Condition

Penn State Cooperative Wetlands Center, Department of Geography, Pennsylvania State University, 302 Walker Building, University Park, PA 16802, USA Center for Statistical Ecology and Environmental Statistics, Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA Department of Forestry, University of Kentucky, 204 T.P. Cooper Building, Lexington, KY 40546-0073, USA Statistics Department, Oregon State University, 44 Kidder Hall, Corvallis, OR 97331, USA School of Forest Resources, Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota Duluth, 1900 East Camp Street, Ely, MN 55731, USA

Bayesian characterization of uncertainty in species interaction strengths

Considerable effort has been devoted to the estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and obtaining point estimates of parameters that contribute to interaction strength magnitudes, leaving the characterization of uncertainty associated with those estimates unconsidered. We consider a means of characterizing the uncertainty of a generalist predator’s interaction strengths by formulating an observational method for estimating a predator’s prey-specific per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. A key insight is the informative nature of several so-called non-informative priors that have been used in modeling the sparse data typical of predator feeding surveys. We introduce to ecology a new neutral prior and provide evidence for its superior performance. We use a case study to consider the attack rates in a New Zealand intertidal whelk predator, and we illustrate not only that Bayesian point estimates can be made to correspond with those obtained by frequentist approaches, but also that estimation uncertainty as described by 95% intervals is more useful and biologically realistic using the Bayesian method. In particular, unlike in bootstrap confidence intervals, the lower bounds of the Bayesian posterior intervals for attack rates do not include zero when a predator–prey interaction is in fact observed. We conclude that the Bayesian framework provides a straightforward, probabilistic characterization of interaction strength uncertainty, enabling future considerations of both the deterministic and stochastic drivers of interaction strength and their impact on food webs.

Isomorphic chain graphs for modeling spatial dependence in ecological data

Graphical models (alternatively, Bayesian belief networks, path analysis models) are increasingly used for modeling complex ecological systems (e.g., Lee, In: Ferson S, Burgman M(eds) Quantative methods for conservation biology. Springer, Berlin Heilin Heideslperk New York, pp.127–147, 2000; Borsuk et al., J Water Res Plann Manage 129:271–282, 2003). Their implementation in this context leverages their utility in modeling interrelationships in multivariate systems, and in a Bayesian implementation, their intuitive appeal of yielding easily interpretable posterior probability estimates. However, methods for incorporating correlational structure to account for observations collected through time and/or space—features of most ecological data—have not been widely studied; Haas et al. (AI Appl 8:15–27, 1994) is one exception. In this paper, an “isomorphic” chain graph (ICG) model is introduced to account for correlation between samples by linking site-specific Bayes network models. Several results show that the ICG preserves many of the Markov properties (conditional and marginal dependencies) of the site-specific models. The ICG model is compared with a model that does not account for spatial correlation. Data from several stream networks in the Willamette River valley, Oregon (USA) are used. Significant correlation between sites within the same stream network is shown with an ICG model.