We describe spatio-temporal random processes using linear mixed models. We show how many commonly used models can be viewed as special cases of this general framework and pay close attention to models with separable or product-sum covariances. The proposed linear mixed model formulation facilitates the implementation of a novel algorithm using Stegle eigendecompositions, a recursive application of the Sherman-Morrison-Woodbury formula, and Helmert-Wolf blocking to efficiently invert separable and product-sum covariance matrices, even when every spatial location is not observed at every time point. We show our algorithm provides noticeable improvements over the standard Cholesky decomposition approach. Via simulations, we assess the performance of the separable and product-sum covariances and identify scenarios where separable covariances are noticeably inferior to product-sum covariances. We also compare likelihood-based and semivariogram-based estimation and discuss benefits and drawbacks of both. We use the proposed approach to analyze daily maximum temperature data in Oregon, USA, during the 2019 summer. We end by offering guidelines for choosing among these covariances and estimation methods based on properties of observed data.

The hydrologic nonstationarity and uncertainty associated with climate change requires new decision-making methods to incorporate climate change impacts into flood frequency and flood risk analyses. To aid decision-making under climate change, we developed a bottom-up approach for assessing the performance of flood management systems under climate uncertainty and nonstationarity. The developed bottom-up approach was applied to the American River, CA, USA flood management system by first identifying the sensitivity and vulnerability of the system to different climates. To do this, we developed a climate response surface by calculating and plotting Expected Annual Damages (EAD, $/year) under different flood regimes. Next, we determined a range of plausible future climate change and flood frequency scenarios by applying Bayesian statistical methods to projected future flows derived from a Variable Infiltration Capacity (VIC) model forced with Global Circulation Model (GCM) output. We measured system robustness as the portion of plausible future scenarios under which the current flood system could meet its performance goal. Using this approach, we then evaluated the robustness of four proposed management strategies in the 2012 Central Valley Flood Protection Plan in terms of both flood risk and cost-effectiveness, to assess the performance of the strategies in the face of climate risks. Results indicated that the high sensitivity of the expected damages to changes in flood regimes makes the system extremely vulnerable to a large portion of the plausible range of future flood conditions. The management strategy that includes a combination of nature-based flood management actions along with engineered structures yields the greatest potential to increase system robustness in terms of maintaining EAD below an acceptable risk threshold. However, this strategy still leaves the system vulnerable to a wide range of plausible future conditions. As flood frequency regimes increase in intensity from the current conditions, the cost-effectiveness of the management strategies increases, to a point, before decreasing. This bottom up analysis demonstrated a viable decision-making approach for water managers in the face of uncertain and changing future conditions. Neglecting to use such an approach and omitting climate considerations from water resource planning could lead to strategies that do not perform as expected or which actually lead to mal-adaptations, increasing vulnerability to climate change.

Life-history traits of individuals in marine populations exhibit large sources of variability. In marine fish, variation of individual size at a given age has three main components: (1) spatial, correlated with the location in which individuals are caught, (2) temporal, correlated with the time when individuals are caught, and (3) generational, correlated with the year of birth of the examined individuals. These variations, if present, have practical implications for individual fitness as well as for sampling, survey design, and population assessment. Disentangling these variations and understanding their sources is hard, given the potentially correlated nature of their effects on individual traits. This study examines the size-at-age relationship of the Bering Sea Pacific cod, an economically and ecologically important groundfish. We used extensive records spanning 1994 to 2016 (inclusive) of 25,213 observations of both environmental variables and catch, lengths, and ages. We found that the average size of individuals of the same age could differ up to 7 cm. Notably, we found that the cohort composition of the sampled population explained >75% of the year effect and that individuals caught in the northwest and shallower portion of the sampling area were on average 5 cm smaller than individuals caught in the southern and deeper portion. We further found that northwest movement of young cod (age 1-5) as a result of warming places individuals in areas where we predict them to have smaller size at age. Smaller and less conditioned individuals are less fecund and may not be able to perform long migrations to return to their distant spawning grounds. Both the spatial distribution and water temperature experienced by Pacific cod in the Bering Sea are changing, and this study provides a mechanism for how these changes affect Pacific cod life-history traits and individual fitness.

Partial migration, the phenomenon in which animal populations are composed of both migratory and nonmigratory individuals, is widespread among migrating animals. The proportion of migrants in these populations has direct influences on population genetics and dynamics, ecosystem dynamics, mating systems, evolution, and responses to environmental change, yet there are very few studies that measure the proportion of migrants. This is because existing methods to estimate the proportion of migrants are time-consuming and expensive. In this paper, we demonstrate a new method for estimating the proportion of migrants in a population based on sex ratio measurements. Many partially migratory taxa exhibit sex-biased migration or residency, and in these cases, the sex ratios of migrants and nonmigrants are fundamentally related to the proportion of migrants in the population. We define this relationship quantitatively and show how it can be used to infer the proportion of migrants in a population through a process we term “sex-ratio balancing”. We obtain Bayesian estimates of proportion of migrants and quantify the uncertainty in these estimates with highest posterior density intervals. Lastly, we validate the sex-ratio balancing approach with a Chinook salmon (Oncorhynchus tshawytscha Walbaum in Artedi, 1792) data set. Sex-ratio balancing holds promise as a tool for quantifying partial migration and filling a key data gap about partially migratory taxa.

Considerable effort has been devoted to the empirical estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and on obtaining point estimates of parameters that contribute to interaction strength magnitude, leaving characterizations of estimation uncertainty and distinctions between the deterministic and stochastic contributions to variation largely unconsidered. Here we consider a means of quantifying interaction strength uncertainty by formulating an observational method for estimating per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. In doing so we highlight the informative nature of several so-called non-informative prior choices in modeling the sparse data typical of predator feeding surveys and provide evidence for the superior performance of a new neutral prior choice. A case study application shows that while Bayesian point estimates may be made to correspond with those obtained by frequentist approaches, estimation uncertainty as described by the 95% intervals is more biologically realistic using the Bayesian method in that the lower bounds of the Bayesian posterior intervals for the attack rates do not include zero when the occurrence of a given predator-prey interaction is in fact observed. This contrasts with bootstrap confidence intervals that often do contain zero in such cases. The Bayesian approach provides a straightforward, probabilistic characterization of interaction strength uncertainty. In doing so it provides a framework for considering both the deterministic and stochastic drivers of species interactions and their impact on food web dynamics.

Functional trait analysis is an appealing approach to study differences among biological communities because traits determine species' responses to the environment and their impacts on ecosystem functioning. Despite a rapidly expanding quantitative literature, it remains challenging to conceptualize concurrent changes in multiple trait dimensions ("trait space") and select quantitative functional diversity methods to test hypotheses prior to analysis. To address this need, we present a widely applicable framework for visualizing ecological phenomena in trait space to guide the selection, application, and interpretation of quantitative functional diversity methods. We describe five hypotheses that represent general patterns of responses to disturbance in functional community ecology and then apply a formal decision process to determine appropriate quantitative methods to test ecological hypotheses. As a part of this process, we devise a new statistical approach to test for functional turnover among communities. Our combination of hypotheses and metrics can be applied broadly to address ecological questions across a range of systems and study designs. We illustrate the framework with a case study of disturbance in freshwater communities. This hypothesis-driven approach will increase the rigor and transparency of applied functional trait studies.

Motivated by an example in marine science, we use Fisher's method to combine independent likelihood ratio tests (LRTs) and asymptotic independent score tests to assess the equivalence of two zero-inflated Beta populations (mixture distributions with three parameters). For each test, test statistics for the three individual parameters are combined into a single statistic to address the overall difference between the two populations. We also develop non parametric and semiparametric permutation-based tests for simultaneously comparing two or three features of unknown populations. Simulations show that the likelihood-based tests perform well for large sample sizes and that the statistics based on combining LRT statistics outperforms the ones based on combining score test statistics. The permutation-based tests have overall better performance in terms of both power and type I error rate. Our methods are easy to implement and computationally efficient, and can be expanded to more than two populations and to other multiple parameter families. The permutation tests are entirely generic and can be useful in various applications dealing with zero (or other) inflation.

10 Considerable effort has been devoted to the empirical estimation of species 11 interaction strengths. This effort has focused primarily on statistical significance 12 testing and on obtaining point estimates of parameters that contribute to interaction 13 strength magnitude, leaving characterizations of estimation uncertainty and 14 distinctions between the deterministic and stochastic contributions to variation 15 largely unconsidered. Here we consider a means of quantifying interaction strength 16 uncertainty by formulating an observational method for estimating per capita attack 17 rates as a Bayesian statistical model. This formulation permits the explicit 18 incorporation of multiple sources of uncertainty. In doing so we highlight the 19 informative nature of several so-called non-informative prior choices in modeling the 20 sparse data typical of predator feeding surveys and provide evidence for the superior 21 performance of a new neutral prior choice. A case study application shows that while 22 Bayesian point estimates may be made to correspond with those obtained by 23 frequentist approaches, estimation uncertainty as described by the 95% intervals is 24 more biologically realistic using the Bayesian method in that the lower bounds of the 25 Bayesian posterior intervals for the attack rates do not include zero when the 26 occurrence of a given predator-prey interaction is in fact observed. This contrasts 27 with bootstrap confidence intervals that often do contain zero in such cases. The 28 Bayesian approach provides a straightforward, probabilistic characterization of 29 interaction strength uncertainty. In doing so it provides a framework for considering 30 both the deterministic and stochastic drivers of species interactions and their impact 31 on food web dynamics. 32

A number of researchers have attempted to estimate salmonid smolt survival during outmigration through an estuary. However, it is currently unclear how the design of such studies influences the accuracy and precision of survival estimates. In this simulation study we consider four patterns of smolt survival probability in the estuary, and test the performance of several different sampling strategies for estimating estuarine survival assuming perfect detection. The four survival probability patterns each incorporate a systematic component (constant, linearly increasing, increasing and then decreasing, and two pulses) and a random component to reflect daily fluctuations in survival probability. Generally, spreading sampling effort (tagging) across the season resulted in more accurate estimates of survival. All sampling designs in this simulation tended to under-estimate the variation in the survival estimates because seasonal and daily variation in survival probability are not incorporated in the estimation procedure. This under-estimation results in poorer performance of estimates from larger samples. Thus, tagging more fish may not result in better estimates of survival if important components of variation are not accounted for. The results of our simulation incorporate survival probabilities and run distribution data from previous studies to help illustrate the tradeoffs among sampling strategies in terms of the number of tags needed and distribution of tagging effort. This information will assist researchers in developing improved monitoring programs and encourage discussion regarding issues that should be addressed prior to implementation of any telemetry-based monitoring plan. We believe implementation of an effective estuary survival monitoring program will strengthen the robustness of life cycle models used in recovery plans by providing missing data on where and how much mortality occurs in the riverine and estuarine portions of smolt migration. These data could result in better informed management decisions and assist in guidance for more effective estuarine restoration projects.

Background Osteosarcoma (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. Results Taurolidine 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. Conclusion Taurolidine’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.

ABSTRACT Ammonia (NH 3 )-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 NH 3 oxidation by the marine thaumarchaeon Nitrosopumilus maritimus was unaffected during a 24-h exposure to ≤20 μM concentrations of 1-alkynes C 8 and C 9 . In contrast, NH 3 oxidation by two AOB ( Nitrosomonas europaea and Nitrosospira multiformis ) was quickly and irreversibly inactivated by 1 μM C 8 (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 NH 4 + -stimulated increases in AOB population densities, but octyne did not prevent increases in AOA population densities that were prevented by acetylene. Furthermore, octyne-resistant, NH 4 + -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 NH 4 + -saturated net nitrification measured in whole soils. Octyne can be useful in short-term assays to discriminate AOA and AOB contributions to soil nitrification.

Forest understory communities are important components in forest ecosystems providing wildlife habitat and influencing nutrient cycling, fuel loadings, fire behavior and tree species composition over time. One of the most widely utilized understory component metrics is understory vegetation cover, often used as a measure of vegetation abundance. To date, understory vegetation cover estimation and prediction has proven to be inherently difficult using traditional explanatory variables such as: leaf area index, basal area, slope, and aspect. We introduce airborne lidar-derived metrics into the modeling framework for understory vegetation cover. A new airborne lidar metric, understory lidar cover density, created by filtering understory lidar points using intensity values increased traditional explanatory power from non-lidar understory vegetation cover estimation models (non-lidar R2-values: 0.2–0.45 vs. lidar R2-values: 0.7–0.8). Beta regression, a relatively new modeling technique for this type of data, was compared with a traditional weighted linear regression model using a leave-one-out cross‐validation procedure. Both models provided similar understory vegetation cover accuracies (±22%) and biases (~0%) using 40.5m2 circular plots (n=154). The method presented in this paper provides the ability to accurately obtain census understory vegetation cover information at fine spatial resolutions over a broad range of stand conditions for the interior ponderosa pine forest type. Additional model enhancement and the extension of the method into other forest types warrant further investigation.

Graphical models provide an important tool for facilitating communication between scientists, decision-makers, and statisticians—many complicated ecological processes can be described in terms of “box-and-arrow” conceptual diagrams (e.g., Shipley in Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inferences, Cambridge Universtiy Press, Cambridge, 2000; Clark and Gelfand TRENDS in Ecology and Evolution 21:375–380, 2006). In particular, problems in landscape ecology often involve modeling relationships among multiple physical and/or biological variables that may operate on differing spatial scales (e.g., Rossi et al. in Ecol Monographs 62:277–314, 1992; Legendre et al. in Ecography 25:601–615, 2002; Overmars et al. in Ecol Model 164:257–270, 2003; Brown and Spector in J Appl Ecol 45:1639–1648, 2008; Koniak and Noy-Meir in Ecol Model 220:1148–1158, 2008). These problems are inherently multivariate, though researchers commonly rely on univariate methods, such as spatial regression models, to address them. In this paper, we introduce a multivariate method—graphical spatial models—that extends path analysis to incorporate spatial autocorrelation in one or more variables in a directed graph. We show how both exogenous and endogenous ecological processes as defined by Legendre et al. (Ecography 25:601–615, 2002) and Lichstein et al. (Ecol Monographs 72:445–463, 2002) can be represented in a graph. Most importantly, we show how to translate graphs representing these ecological processes into statistically estimable models. We motivate our theoretical results using an example of stream health data from the Willamette Valley, Oregon. For these data we are interested in the spatial pattern within both riparian land use and an index of stream health, and whether there is an association between land use and stream health, after accounting for these spatial patterns. We use a graphical spatial model to address these ecological questions simultaneously. We find that the health of a stream decreases as the percent of developed land within a 120-m riparian buffer increases; interestingly, there is only evidence of spatial pattern within land use.

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 Onchorhynchus tshawytscha and steelhead O. mykiss were implanted with uniquely coded radio or acoustic transmitter Lags 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. whereas estimates for fall Chinook salmon were comparatively low. This order of high to low survival for SS Chinook salmon-steelhead, and fall Chinook salmon corresponds with the chronology of out-migration timing and waning rivet flows. After accounting for it number of factors. estimated survival between Bonneville Dam and the tipper-estuary site did not differ significantly between steelhead that were barged and those that migrated in-river during 2002 and 2003. Conversely, estimated survival to the upper-estuary site was quite low for barged fall Chinook salmon in comparison with fish migrating in-river during the middle and late release dates in 2003. Hypotheses for the low survival of barged fall Chinook salmon are presented and discussed.

OBJECTIVE:To determine the macroscopic effects of topical application of taurolidine on second intention healing of experimentally induced wounds in rats.ANIMALS:32 adult Sprague-Dawley female rats.PROCEDURES:In each rat, 2 skin wounds were created in the lumbar area. Groups of 7 rats were assigned to have 1 wound treated topically with hydroxycellulose gel (HDCG), 2% taurolidine in HDCG (T-HDCG), 2% taurolidine-sodium citrate solution, or bacitracin-neomycin-polymyxin B ointment; the other wound was not treated. Four control rats (8 untreated wounds) were used. Wounds were monitored for contraction, epithelialization, and complete healing at 4, 8, and 14 days after wound creation. The number of days to complete healing was also recorded for each wound.RESULTS:Compared with other treatments or untreated wounds, wounds treated with T-HDCG had decreased total healing at day 8 and decreased epithelialization and decreased total healing at day 14. Wounds treated with T-HDCG required approximately 3 days longer to completely heal than all other treated and untreated wounds. Application of bacitracinneomycin-polymyxin B ointment did not enhance wound healing. Mean time to complete healing of untreated wounds in all treatment and control groups was 10.00 to 10.14 days.CONCLUSIONS AND CLINICAL RELEVANCE:In rats, topical application of T-HDCG to wounds had a negative effect on second intention healing by delaying the epithelialization process. In mammals, generally, wounds treated topically with taurolidine may need to be treated and monitored for a longer period than other wounds treated with other common woundhealing compounds or untreated wounds.

Hierarchical biological scales permeate research in tree physiology and represent multiple sources of variation. We discuss the importance of matching the sampling and analysis scales to biological scales in the data. The advantages of statistical hierarchical modeling are demonstrated using the relationship between specific conductivity and tracheid diameter of secondary xylem as an example. The structure and results of three statistical models were compared within a Bayesian context: a simple linear regression (SLR); a repeated measures analysis (REP); and a hierarchical model (HM). The models share similar mean structures but differ in how variation is partitioned among scales: the SLR model assumes independence among observations (variation came from only a single scale); the REP allows multiple observations of each tree to be correlated; and the HM incorporates features of the REP with an additional variance structure that partitions variation across a broader scale. Our data included hierarchical scales of position on the tree, tree, fertilization treatment and species (Pseudotsuga menziesii (Mirb.) Franco). The HM gave more precise estimates for model parameters, was more robust to outliers, provided a more detailed description of covariances within the data at multiple scales compared with the SLR and REP and increased our ability to detect differences among positions on the tree. The proper statistical analyses increase the value of research by allowing the most exact interpretation.

Frogs exhibit extreme plasticity and individual variation in growth and behavior during metamorphosis, driven by interactions of intrinsic state factors and extrinsic environmental factors. In northern red-legged frogs ( Rana aurora Baird and Girard, 1852), we studied the timing of departure from the natal pond as it relates to date and size of individuals at metamorphosis in the context of environmental uncertainty. To affect body size at metamorphosis, we manipulated food availability during the larval stage for a sample (317) of 1045 uniquely marked individuals and released them at their natal ponds as newly metamorphosed frogs. We recaptured 34% of marked frogs in pitfall traps as they departed and related the timing of their initial terrestrial movements to individual properties using a time-to-event model. Median age at first capture was 4 and 9 days postmetamorphosis at two sites. The rate of departure was positively related to body size and to date of metamorphosis. Departure rate was strongly negatively related to time elapsed since rainfall, and this effect was diminished for smaller and later metamorphosing frogs. Individual variation in metamorphic traits thus affects individuals’ responses to environmental variability, supporting a behavioral link with variation in survival associated with these same metamorphic traits.

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.

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.