Analysis of the global warming dynamics from temperature time series

Global warming is the observed increase of the average temperature of the Earth. The primary cause of this phenomenon is the release of the greenhouse gases by burning of fossil fuels, land cleaning, agriculture, among others, leading to the increase of the so-called greenhouse effect. An approach to deal with this important problem is the time series analysis. In this regard, different techniques can be applied to evaluate the global warming dynamics. This kind of analysis allows one to make better predictions increasing our comprehension of the phenomenon. This article applies nonlinear tools to analyze temperature time series establishing state space reconstruction and prediction. Since noise contamination is unavoidable in data acquisition, it is important to employ robust techniques. The method of delay coordinates is employed for state space reconstruction and delay parameters are evaluated using the method of average mutual information and the method of false nearest neighbors. Afterwards, the simple nonlinear prediction method is employed to estimate temperatures of the future. Temperature time series from different places of the planet are used. Initially, the approach is verified considering known parts of the time series and afterwards, results are extrapolated for future values estimating temperature until 2028. Results show that these techniques are interesting to estimate temperature time history, presenting coherent estimations.     

Relationships between dissolved and particulate carbohydrate temporal patterns and mucilaginous aggregates formation: evidences from time series analysis and biochemical composition of aggregates

A 3-year monitoring of dissolved and particulate carbohydrate concentrations in four transects located in the Adriatic and Tyrrhenian Seas was performed in order to get information on the role played by carbohydrates in the mucilage formation in these areas. The application of time series analysis pointed out that the concentration of dissolved carbohydrates does not vary significantly in coincidence of the mucilage appearance showing an almost constant state with respect to time. In contrast, wider temporal variations of carbohydrate amounts, either increase or decrease, were observed when mucilages were lacking or reduced. This almost constant state of carbohydrate amounts observed in presence of mucilages that we define 'steady state' could be associated to an alteration of the complex chemical equilibrium between synthesis and degradation (either hydrolysis or oxidation) reactions of the organic polymers which are typical of the humification processes in the marine environment. The results of this study suggest that the monitoring of carbohydrates can represent an useful tool for the comprehension of the most relevant phenomena of mucilage appearance in the Northern Adriatic Sea.     

Using a model selection criterion to identify appropriate complexity in aquatic biogeochemical models

Aquatic biogeochemical models are widely used as tools for understanding aquatic ecosystems and predicting their response to various stimuli (e.g., nutrient loading, toxic substances, climate change). Due to the complexity of these systems, such models are often elaborate and include a large number of estimated parameters. However, correspondingly large data sets are rarely available for calibration purposes, leading to models that may be overfit and possess reduced predictive capabilities. We apply, for the first time, information-theoretic model-selection techniques to a set of spatially explicit (1D) algal dynamics models of varying parameter dimension. We demonstrate that increases in complexity tend to produce a better model fit to calibration data, but beyond a certain degree of complexity the benefits of adding parameters are diminished (the risk of overfitting becomes greater). The particular approach taken here is computationally expensive, but several suggestions are made as to how multimodel methods may practically be extended to more sophisticated models.     

Nest selection by snow petrels Pagodroma nivea in East Antarctica: Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas.From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability.We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix.     

Sexual selection in a socially monogamous bird: male color predicts paternity success in the mountain bluebird, <Emphasis Type="Italic">Sialia currucoides</Emphasis>

Ornamental traits are thought to evolve because they give individuals an advantage in securing multiple mates. Thus, the presence of ornamentation among males in many monogamous bird species presents something of a conundrum. Under certain conditions, extra-pair paternity can increase the variance in reproductive success among males, thus increasing the potential for sexual selection to act. We addressed this possibility in the mountain bluebird (Sialia currucoides), a socially monogamous songbird in which males possess brilliant ultraviolet (UV)-blue plumage. Specifically, we asked whether a male’s success at siring offspring within his own nest and within the nests of other males was related to his coloration. In pairwise comparisons, males that sired extra-pair offspring were not more colorful than the males that they cuckolded. However, males that sired at least one extra-pair offspring were, on average, brighter and more UV-blue than males that did not sire extra-pair offspring. Brighter, more UV-blue males sired more offspring both with their own mate and tended to sire more offspring with extra-pair mates and thus sired more offspring overall. Our results support the hypothesis that the brilliant UV-blue ornamental plumage of male mountain bluebirds evolved at least in part because it provides males with an advantage in fertilizing the eggs of multiple females.     

Monitoring the growth of American beech affected by beech bark disease in Maine using the Kalman filter

The effect of beech bark disease on tree growth was tracked using paired resistant and susceptible American beech trees in two locations in Maine. Within each site, the paired trees were chosen in close proximity and with similar morphological characteristics (e.g. stem diameter and crown class) to minimize environment effects in subsequent analysis. A Kalman filter approach was employed to analyse the yearly time-dependent mean differences between paired susceptible and resistant tree-ring widths using simple structural time series models in state space form. On one site, under the influence of a moderate maritime climate, stand dynamics is hypothesized to account for the 34 year difference in onset of decline of trees in codominant, versus those in the intermediate crown classes. The harsher winter conditions associated with the second, more northerly site and known to limit the insect component of the disease complex, are hypothesized to be more of a factor in the close (six year) difference in decline onset between the two crown classes on this site. Some strengths and cautions in the Kalman filter approach are discussed in relation to the analysis of time-dependent trends in tree-ring series.     

Changes over time in persistent organic pollutants (POP) concentrations in human milk in the Republic of Moldova

Persistent organic pollutants (POP) in human milk originating from various sources are used as environmental exposure markers. Further, mothersmilk is a predominant pathway for human exposure for suckling infants to POP. The aim of present study was to provide baseline and time trend information on human milk POP concentrations in the Republic of Moldova. A pooled sample of breast milk was analyzed in 2009 and 2015 and health risk evaluated by comparing reference values of humans' daily intake with estimated daily intake (EDI). Results showed decreases for concentrations of dichlorodiphenyltrichloroethane, hexachlorobenzene, dieldrin, heptachlor, α and β-hexachlorocyclohexane, polychlorinated biphenyls, dioxin-like polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins and dibenzofurans between sampling periods. Regardless of the decrease, EDI was higher than reference values for dioxins toxic equivalents, polychlorinated biphenyls and β-hexachlorocyclohexane in both phases. However, increased levels were detected for polybrominated diphenyl ethers (twofold) and hexabromocyclododecane (threefold) from 2009 to 2015, but the EDI in both phases is much lower than the reference. The decline in certain POP levels may be attributed to coordinated efforts undertaken by national and international entities to regulate these compounds. The basis for the elevated levels of polybrominated diphenyl ethers and hexabromocyclododecane remains unknown but raises concerns regarding lactational exposure.