Testing a pyroclimatic hypothesis on the Mexico-United States border

The "pyroclimatic hypothesis" proposed by F. Biondi and colleagues provides a basis for testable expectations about climatic and other controls of fire regimes. This hypothesis asserts an a priori relationship between the occurrence of widespread fire and values of a relevant climatic index. Such a hypothesis provides the basis for predicting spatial and temporal patterns of fire occurrence based on climatic control. Forests near the Mexico-United States border offer a place to test the relative influence of climatic and other controls in mountain ranges that are ecologically similar and subject to broadly similar top-down climatic influence, but with differing cultural influences. We tested the pyroclimatic hypothesis by comparing fire history information from the Mesa de las Guacamayas, a mountain range in northwestern Chihuahua, with previously published fire data from the Chiricahua Mountains, in southeastern Arizona, approximately 150 km away. We developed a priori hypothetical models of fire occurrence and compared their performance to empirical climate-based models. Fires were frequent at all Mesa de las Guacamayas study sites through the mid-20th century and continued uninterrupted to the present at one site, in contrast to nearly complete fire exclusion after 1892 at sites in the Chiricahua Mountains. The empirical regression models explained a higher proportion of the variability in fire regime associated with climate than did the a priori models. Actual climate-fire relationships diverged in each country after 1892. The a priori models predicted continuing fires at the same rate per century as prior to 1892; fires did in fact continue in Mexico, albeit with some alteration of fire regimes, but ceased in the United States, most likely due to changes in land use. The cross-border comparison confirms that a frequent-fire regime could cease without a climatic cause, supporting previous arguments that bottom-up factors such as livestock grazing can rapidly and drastically alter surface fire regimes. Understanding the historical patterns of climate controls on fire could inform the use of historical data as ecological reference conditions and for future sustainability.     

Dike-break induced flows: a simplified model

A simplified model for the prediction of the steady-state outflow through a breach in an inland dike is presented. It consists in the application of the mass and momentum conservation principles to a macroscopic control volume. A proper definition of the shape of the control volume enables to take the main characteristics of the flow into account and thus to compensate for the extreme simplification of the spatial representation of the model. At the breach, a relation derived from the shallow-water equations is used to determine the direction of the flow. Developments have been guided by numerical simulations and results have been compared to experimental data. Both the accuracy and the domain of validity of the simplified model are found satisfactory.     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

Using information theory as a substitute for stepwise regression in ecology and behavior

In ecological and behavioral research, drawing reliable conclusions from statistical models with multiple predictors is usually difficult if all predictors are simultaneously in the model. The traditional way of handling multiple predictors has been the use of threshold-based removal-introduction algorithms, that is, stepwise regression, which currently receives considerable criticism. A more recent and increasingly propagated modelling method for multiple predictors is the information theoretic (IT) approach that quantifies the relative suitability of multiple, potentially non-nested models based on a balance of model fit and the accuracy of estimates. Here, we examine three shortcomings of stepwise regression, subjective critical values, model uncertainty, and parameter estimation bias, which have been suggested to be avoided by applying information theory. We argue that, in certain circumstances, the IT approach may be sensitive to these issues as well. We point to areas where further testing and development could enhance the performance of IT methods and ultimately lead to robust inferences in behavioral ecology.     

Model weights and the foundations of multimodel inference

Statistical thinking in wildlife biology and ecology has been profoundly influenced by the introduction of AIC (Akaike's information criterion) as a tool for model selection and as a basis for model averaging. In this paper, we advocate the Bayesian paradigm as a broader framework for multimodel inference, one in which model averaging and model selection are naturally linked, and in which the performance of AIC-based tools is naturally evaluated. Prior model weights implicitly associated with the use of AIC are seen to highly favor complex models: in some cases, all but the most highly parameterized models in the model set are virtually ignored a priori. We suggest the usefulness of the weighted BIC (Bayesian information criterion) as a computationally simple alternative to AIC, based on explicit selection of prior model probabilities rather than acceptance of default priors associated with AIC. We note, however, that both procedures are only approximate to the use of exact Bayes factors. We discuss and illustrate technical difficulties associated with Bayes factors, and suggest approaches to avoiding these difficulties in the context of model selection for a logistic regression. Our example highlights the predisposition of AIC weighting to favor complex models and suggests a need for caution in using the BIC for computing approximate posterior model weights.     

Software sensor design based on empirical data

Software sensor design consists of building an estimate of some quantity of interest. This estimate can be used either to replace a physical measurement, or to validate an existing one. This paper provides some general guidelines for the design of software sensors based on empirical data. When the model is a priori unknown, the problem can be stated in terms of non-parametric regression or black-box modelling. Complexity control is the main difficulty in this setting. A trade-off must be achieved between two antagonist goals: the model should not be too simple, and model identification should not be too variable. We propose to address this issue by a penalization algorithm, which also estimates the relevance of input features in the identification process. A data-driven software sensor should also provide accuracy and validity indexes of its prediction. We show how these indexes can be estimated for complex non-parametric methods, such as neural networks. An application in environmental monitoring, the design of an ammonia software sensor, illustrates each step of the approach.     

Bridging gaps in demographic analysis with phylogenetic imputation

Phylogenetically informed imputation methods have rarely been applied to estimate missing values in demographic data but may be a powerful tool for reconstructing vital rates of survival, maturation, and fecundity for species of conservation concern. Imputed vital rates could be used to parameterize demographic models to explore how populations respond when vital rates are perturbed. We used standardized vital rate estimates for 50 bird species to assess the use of phylogenetic imputation to fill gaps in demographic data. We calculated imputation accuracy for vital rates of focal species excluded from the data set either singly or in combination and with and without phylogeny, body mass, and life-history trait data. We used imputed vital rates to calculate demographic metrics, including generation time, to validate the use of imputation in demographic analyses. Covariance among vital rates and other trait data provided a strong basis to guide imputation of missing vital rates in birds, even in the absence of phylogenetic information. Mean NRMSE for null and phylogenetic models differed by <0.01 except when no vital rates were available or for vital rates with high phylogenetic signal (Pagel's λ > 0.8). In these cases, including body mass and life-history trait data compensated for lack of phylogenetic information: mean normalized root mean square error (NRMSE) for null and phylogenetic models differed by <0.01 for adult survival and <0.04 for maturation rate. Estimates of demographic metrics were sensitive to the accuracy of imputed vital rates. For example, mean error in generation time doubled in response to inaccurate estimates of maturation time. Accurate demographic data and metrics, such as generation time, are needed to inform conservation planning processes, for example through International Union for Conservation of Nature Red List assessments and population viability analysis. Imputed vital rates could be useful in this context but, as for any estimated model parameters, awareness of the sensitivities of demographic model outputs to the imputed vital rates is essential.     

The efficiency of paying compensation in the Pigovian solution to externality problems

This note examines the efficiency of giving the victim of an externality the revenue raised by a tax on the injurer when the injurer and victim bargain with each other and act strategically. If the government has full information about the externality problem, then compensation is compatible with, but is not required for, the attainment of efficiency. If the government has limited information, then the desirability of compensation depends on the parties' behavior. In general, there is not an a priori case against compensation.     

Use of Basic Biological Information for Rapid Prediction of the Response of Species to Habitat Loss

Abstract:  Much research has focused on identifying traits that can act as useful indicators of how habitat loss affects the extinction risk of species, and the results are mixed. We developed 2 simple, rapid-assessment models of the susceptibility of species to habitat loss. We based both on an index of range size, but one also incorporated an index of body mass and the other an index combining habitat and dietary specialization. We applied the models to samples of birds (Accipitridae and Bucerotidae) and to the lemurs of Madagascar and compared the models' classifications of risk with the IUCN's global threat status of each species. The model derived from ecological attributes was much more robust than the one derived from body mass. Ecological attributes identified threatened birds and lemurs with an average of 80% accuracy and endangered and critically endangered species with 100% accuracy and identified some species not currently listed as threatened that almost certainly warrant conservation consideration. Appropriate analysis of even fairly crude biological information can help raise early-warning flags to the relative susceptibilities of species to habitat loss and thus provide a useful and rapid technique for highlighting potential species-level conservation issues. Advantages of this approach to classifying risk include flexibility in the specialization parameters used as well as its applicability at a range of spatial scales.     

Sensitivity of a crop growth simulation model to variation in LAI and canopy nitrogen used for run-time calibration

Run-time calibration, i.e. adjusting simulation results for field observations of model driving variables during run-time, may allow correcting for deviations between complex mechanistic simulation model results and actual field conditions. Leaf area index (LAI) and canopy nitrogen contents (LeafNWt) are the most important driving variables for these models, as they govern light interception and photosynthetic production capacity of the crop. Remote sensing may provide (spatial) data from which such information can be estimated. How, when and at what frequency such additional information is integrated in the simulation process may have various effects on the simulations. The objective of this study was to quantify the effects of different run-time calibration scenarios for final grain yield (FGY) simulations in order to optimize remote sensing image (RS) acquisition. The PlantSys model was calibrated on LAI and LeafNWt for maize in France and used to simulate maize crop growth in the Argentina and the USA, for which non-destructive estimates of LAI and leaf chlorophyll contents were acquired by optical measurement techniques. Leaf chlorophyll data were used to estimate LeafNWt. Due to its structure, the PlantSys model was more susceptible to run-time calibration with LeafNWt than with LAI. Run-time calibration with LAI showed the largest effect on FGY before and around flowering, and could mainly be related to maintenance respiration costs. Run-time calibration with LeafNWt showed the largest effect on FGY at and after flowering and could mainly be related to the change in effective radiation interception due to change in leaf life. The accuracy of LAI estimates showed a major effect on FGY for underestimations but was small in absolute sense. The accuracy of LeafNWt estimates had significant impact at all crop development stages, but was the strongest after flowering where crop growth and nitrogen uptake are less able to recuperate from changes in LeafNWt. In absolute sense, the effect on FGY was as strong as the accuracy of the LeafNWt estimates when applied in the early reproductive stages. Based on these results it was concluded that remotely sensed in-field variability of LAI and LeafNWt is valuable information that can be used to spatially differentiate model simulations. Run-time calibration at sub-field level may lead to more accurate simulation results for whole fields.     

Towards a new physical theory of biotic evolution and development

Motivated by the postulates:

(a)

Matter has complementary properties: matter waves at high mass density and information oscillations at high information density.

(b)

Biological systems are information generating systems, i.e., genome capacity to generate developmental functional complexity (phenotype).

(c)

Biological information sustains the living state.

We discuss a definite model in which the life state of an organism, for successive generations, is a generalized Schrödinger's type of system which is non-conservative, nonlinear and irreversible. Vitality, the state variable, is the genome capacity to generate developmental functional complexity (phenotype). It is a function of the phenotypic variables of biological information, i.e., matter-energy growth function, life expectancy and natality.We also discuss evolution within this model. We find that the evolution of a unicellular organism, being a process through which the life function undertakes negative damping, leads to the increase of total vitality. Total vitality is a function of the system's lifespan, bio-complexity and total matter-energy metabolized or body mass. Total vitality is quantized. The derived quantization relationship provides plausible theoretical basis for punctuated equilibrium. Total bio-information (total vitality + total natality density) is conserved. The conserved quantity describes evolution and generates ecosystem, population and organism growth dynamics. Vitality has the dimension of flow-bits. Finally, a thought experiment is proposed to falsify the hypothesis.     

Does scale matter in predicting species distributions? case study with the Marbled Murrelet.

Hierarchical selection orders (selection of microsite, patch, home range, population block, and geographic range) are ideal for dictating spatial grain and extent of animal habitat models, but the resultant conditional models are poor for creating predictive maps. I proposed a two-step approach for accurately mapping probability of animal use that incorporates a single-grain analysis of each selection order in the first step and creates a multi-grain model that combines key variables from each selection order in the second step. Such two-step multi-grain models are strongly recommended because they allow interpretation of the scale of selection for a variable. Using a large data set for the Marbled Murrelet (Brachyramphus marmoratus) as a case study and five selection orders, information theory criteria provided strong support that such models are superior to simpler one-step single-grain models for the murrelet. However, a single-grain model can produce high classification accuracy if it represents the most limiting scale. Notably, accuracy of the two-step multi-grain model was no better than a traditional one-step multi-grain model that ignores selection orders, indicating the advantage of two-step modeling is in elucidating scaling effects, not necessarily in improving accuracy of species distribution maps.     

Herbivores enable plant survival under nutrient limited conditions in a model grazing system

We make a theoretical study of nitrogen cycling in a model of a grazing system with five compartments. The rates of uptake of nutrient by plants and herbivores are allowed nonlinear forms which involve no a priori assumptions about whether the system is subject to top-down or bottom-up control. We derive a method of piecewise linear approximation which allows analytical study of the system. We then use this method to investigate the properties of the equilibrium states of the system, and in particular whether the system favours donor- or recipient-control, the grazing optimization problem, and the potential benefits of herbivory to plant growth. We are able to generalise our results to all uptake functions of the same qualitative class as those considered, and to show that in general the system will tend to a stable equilibrium state of donor-controlled herbivory. In this model, the presence of the ‘right’ class of herbivore is not only beneficial to plant growth in certain circumstances, but can be essential to their survival, allowing plants to co-exist with herbivores under conditions in which they would be unable to survive alone.     

Eliciting expert knowledge in conservation science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.     

Optimal divorce and re-mating strategies for monogamous female birds: a simulation model

Although the extensive variation in divorce rates among monogamous bird species has stimulated several theoretical accounts, the mechanisms underlying divorce strategies remain poorly understood. Here, we use an individual-based simulation model to investigate the adaptiveness of mechanisms of mate choice in the context of remating. Our model compares the fitness of females that choose a mate during each breeding season using one of two different decision rules; best-of-n females sample n potential partners and then select the male with the highest quality, whereas better option females choose a mate whose quality is maximal among the non-mated individuals they sampled the season before. It is assumed in the model that best-of-n females have no a priori information about the quality of potential partners and systematically decide to divorce at the beginning of each breeding season before searching for a new mate. Conversely, better option females use the information they gained the season before, and may retain their previous partner if they have no opportunity to mate with an individual of better quality. Results from simulations indicate that the best-of-n decision rule should be favoured when there is a large variation in male quality and low costs of mate sampling. On the other hand, the probability that the better option rule may invade the population is predicted to increase with male survival rate. However, changes in male mortality had no marked influence on the expected proportion of divorcing pairs, contrary to previous theoretical expectations.Communicated by H. Kokko     

Animal dispersal modelling: Handling landscape features and related animal choices

Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled.We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path.     

The ecological impact of invasive cane toads on tropical snakes: field data do not support laboratory-based predictions

Predicting which species will be affected by an invasive taxon is critical to developing conservation priorities, but this is a difficult task. A previous study on the impact of invasive cane toads (Bufo marinus) on Australian snakes attempted to predict vulnerability a priori based on the assumptions that any snake species that eats frogs, and is vulnerable to toad toxins, may be at risk from the toad invasion. We used time-series analyses to evaluate the accuracy of that prediction, based on >3600 standardized nocturnal surveys over a 138-month period on 12 species of snakes and lizards on a floodplain in the Australian wet-dry tropics, bracketing the arrival of cane toads at this site. Contrary to prediction, encounter rates with most species were unaffected by toad arrival, and some taxa predicted to be vulnerable to toads increased rather than declined (e.g., death adder Acanthophis praelongus; Children's python Antaresia childreni). Indirect positive effects of toad invasion (perhaps mediated by toad-induced mortality of predatory varanid lizards) and stochastic weather events outweighed effects of toad invasion for most snake species. Our study casts doubt on the ability of a priori desktop studies, or short-term field surveys, to predict or document the ecological impact of invasive species.     

Instantaneous transport of a passive scalar in a turbulent separated flow

The results of large-eddy simulations of flow and transient solute transport over a backward facing step and through a 180° bend are presented. The simulations are validated successfully in terms of hydrodynamics and tracer transport with experimental velocity data and measured residence time distribution curves confirming the accuracy of the method. The hydrodynamics are characterised by flow separation and subsequent recirculation in vertical and horizontal directions and the solute dispersion process is a direct response to the significant unsteadiness and turbulence in the flow. The turbulence in the system is analysed and quantified in terms of power density spectra and covariance of velocity fluctuations. The injection of an instantaneous passive tracer and its dispersion through the system is simulated. Large-eddy simulations enable the resolution of the instantaneous flow field and it is demonstrated that the instabilities of intermittent large-scale structures play a distinguished role in the solute transport. The advection and diffusion of the scalar is governed by the severe unsteadiness of the flow and this is visualised and quantified. The analysis of the scalar mass transport budget quantifies the mechanisms controlling the turbulent mixing and reveals that the mass flux is dominated by advection.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.     

Integrated modelling of foraging behaviour, energy budget and memory properties

A predator's foraging performance is related to its ability to acquire sufficient information on environmental profitability. This process can be affected by the patchy distribution and clustering of food resources and by the food intake process dynamics.We simulated body mass growth and behaviour in a forager acting in a patchy environment with patchy distribution of both prey abundance and body mass by an individual-based model. In our model, food intake was a discrete and stochastic process and leaving decision was based on the estimate of net energy gain and searching time during their foraging activities. The study aimed to investigate the effects of learning processes and food resource exploitation on body mass and survival of foragers under different scenarios of intra-patch resource distribution.The simulation output showed that different sources of resource variability between patches affected foraging efficiency differently. When prey abundance varied across patches, the predator stayed longer in poorest patches to obtain the information needed and its performance was affected by the cost of sampling and the resulting assessment of the environment proved unreliable. On the other hand, when prey body mass, but not abundance, varied among the patches the predator was quickly able to assess local profitability. Both body mass and survival of the predator were greatly affected by learning processes and patterns of food resource distribution.