Finite area smoothing with generalized distance splines

Most conventional spatial smoothers smooth with respect to the Euclidean distance between observations, even though this distance may not be a meaningful measure of spatial proximity, especially when boundary features are present. When domains have complicated boundaries leakage (the inappropriate linking of parts of the domain which are separated by physical barriers) can occur. To overcome this problem, we develop a method of smoothing with respect to generalized distances, such as within domain distances. We obtain the generalized distances between our points and then use multidimensional scaling to find a configuration of our observations in a Euclidean space of 2 or more dimensions, such that the Euclidian distances between points in that space closely approximate the generalized distances between the points. Smoothing is performed over this new point configuration, using a conventional smoother. To mitigate the problems associated with smoothing in high dimensions we use a generalization of thin plate spline smoothers proposed by Duchon (Constructive theory of functions of several variables, pp 85–100, 1977). This general method for smoothing with respect to generalized distances improves on the performance of previous within-domain distance spatial smoothers, and often provides a more natural model than the soap film approach of Wood et al. (J R Stat Soc Ser B Stat Methodol 70(5):931–955, 2008). The smoothers are of the linear basis with quadratic penalty type easily incorporated into a range of statistical models.     

Ecodynamics: Towards an evolutionary thermodynamics of ecosystems

This paper collects, with few minor formal changes, two of the latest scientific contributions ( [Tiezzi, 2006a] and [Tiezzi et al., 2010]) written by Prof. Enzo Tiezzi, where he introduced new concepts and tools to formalize and understand the role of thermodynamics in ecosystems theory.Particular attention is devoted to goal functions, to the relation of matter, energy, space and time and to the interdisciplinary approach connecting thermodynamics and biology. Entropy is discussed as a fundamental goal function in the far from equilibrium framework. The relationship between entropy, as a non-state function, and the state-function energy is stressed and discussed, at the light of the role of information. The theory of probability is also discussed in the light of new theoretical findings related to the role of events, also in terms of entropy and evolutionary thermodynamics.Confined Ontic Open Systems (COOS) represent the latest model proposed by Prof. Tiezzi based on his Ecodynamic theory, evolutionary thermodynamics, Ulanowicz's ontic. The model has a wide range of applications, including ecosystems, ecological economics, urban organization, the supra-molecular structure of water and global biosphere's models. The model is explained in terms of evolutionary thermodynamics and Jørgensen's ecosystems theory.     

A novel method for investigating the collective behaviour of fish: introducing ‘Robofish’

Collective animal behaviour has attracted much attention recently, but cause-and-effect within interaction sequences has often been difficult to establish. To tackle this problem, we constructed a robotic fish (‘Robofish’) with which three-spined sticklebacks (Gasterosteus aculeatus L.) interact. Robofish is a computer-controlled replica stickleback that can be programmed to move around a tank. First, we demonstrated the functioning of the method: that the sticklebacks interacted with Robofish. We examined two types of interaction: recruitment and leadership. We found that Robofish could recruit a single fish from a refuge and could initiate a turn in singletons and in groups of ten, i.e. act as a leader. We also showed that the influence of Robofish diminished after the first 30 min that fish spent in a new environment. Second, using this method, we investigated the effects of metric and topological inter-individual distance on the influence that Robofish had on the orientation of fish in a shoal of ten. We found that inter-individual interactions during this turn were predominantly mediated by topological, rather than metric, distance. Finally, we discussed the potential of this novel method and the importance of our findings for the study of collective animal behaviour.     

Deterministic approach to the study of the interaction predator-prey in a chemostat with predator mutual interference. Implications for the paradox of enrichment

Our understanding of predator-prey systems has progressed in recent decades mainly due to the ability to test models in chemostats. This study aimed to develop a deterministic model using differential equations to reproduce the dynamics of the interaction of a predator and a prey in a two stage chemostat focusing in the proposed previous prey dependent model of Fussmann et al. (2000) [Fussmann, G.F., Ellner, S.P., Shertzer, K.W., Hairston Jr., N.G., 2000. Crossing the Hopf bifurcation in a live predator-prey system. Science 290, 1358-1360]. The main problem with that model, but parameterized with the values obtained in this study (particularly the concentration of nutrient), was that the temporal trajectory of both the prey and the predator showed very high peaks that eventually led to the extinction of predator in all cases. In the same way the experimental time series obtained in this study does not exhibit the behavior predicted by the model of Fussman et al. On the contrary, as prey density increases, the system actually becomes more stable. Finally, the model that best explained the behavior of the predator and prey in the chemostat, at medium to high dilution rates, was the ratio dependent (algae-nitrogen) model with mutual interference measured in the chemostat (rotifer-alga) and that incorporated the age structure of the predator. Qualitative analysis of the dynamic behavior enabled evaluation of coexistence at equilibrium, coexistence on limit cycles, extinction of the predator or extinction of both populations.     

Effects of Customary Marine Closures on Fish Behavior,Spear‐Fishing Success,and Underwater Visual Surveys

Abstract: Customary management systems (i.e., management systems that limit the use of marine resources), such as rotational fisheries closures, can limit harvest of resources. Nevertheless, the explicit goals of customary management are often to influence fish behavior (in particular flight distance, i.e., distance at which an organism begins to flee an approaching threat), rather than fish abundance. We explored whether the flight distance of reef fishes targeted by local artisanal fishers differed between a customary closure and fished reefs. We also examined whether flight distance of these species affected fishing success and accuracy of underwater visual census (UVC) between customary closed areas and areas open to fishing. Several species demonstrated significant differences in flight distance between areas, indicating that fishing activity may increase flight distance. These relatively long flight distances mean that in fished areas most target species may stay out of the range of spear fishers. In addition, mean flight distances for all species both inside and outside the customary‐closure area were substantially smaller than the observation distance of an observer conducting a belt‐transect UVC (mean [SE]= 8.8 m [0.48]). For targeted species that showed little ability to evade spear fishers, customary closures may be a vital management technique. Our results show that customary closures can have a substantial, positive effect on resource availability and that conventional UVC techniques may be insensitive to changes in flight behavior of fishes associated with fishing. We argue that short, periodic openings of customary closures may allow the health of the fish community to be maintained and local fishers to effectively harvest fishes.     

Antipredator strategies of house finches: are urban habitats safe spots from predators even when humans are around?

Urbanization decreases species diversity, but it increases the abundance of certain species with high tolerance to human activities. The safe-habitat hypothesis explains this pattern through a decrease in the abundance of native predators, which reduces predation risk in urban habitats. However, this hypothesis does not consider the potential negative effects of human-associated disturbance (e.g., pedestrians, dogs, cats). Our goal was to assess the degree of perceived predation risk in house finches (Carpodacus mexicanus) through field studies and semi-natural experiments in areas with different levels of urbanization using multiple indicators of risk (flock size, flight initiation distance, vigilance, and foraging behavior). Field studies showed that house finches in more urbanized habitats had a greater tendency to flock with an increase in population density and flushed at larger distances than in less urbanized habitats. In the semi-natural experiment, we found that individuals spent a greater proportion of time in the refuge patch and increased the instantaneous pecking rate in the more urbanized habitat with pedestrians probably to compensate for the lower amount of foraging time. Vigilance parameters were influenced in different ways depending on habitat type and distance to flock mates. Our results suggest that house finches may perceive highly urbanized habitats as more dangerous, despite the lower number of native predators. This could be due to the presence of human activities, which could increase risk or modify the ability to detect predators. House finches seem to adapt to the urban environment through different behavioral strategies that minimize risk.     

Validating graph-based connectivity models with independent presence–absence and genetic data sets

Habitat connectivity is a key objective of current conservation policies and is commonly modeled by landscape graphs (i.e., sets of habitat patches [nodes] connected by potential dispersal paths [links]). These graphs are often built based on expert opinion or species distribution models (SDMs) and therefore lack empirical validation from data more closely reflecting functional connectivity. Accordingly, we tested whether landscape graphs reflect how habitat connectivity influences gene flow, which is one of the main ecoevolutionary processes. To that purpose, we modeled the habitat network of a forest bird (plumbeous warbler [Setophaga plumbea]) on Guadeloupe with graphs based on expert opinion, Jacobs’ specialization indices, and an SDM. We used genetic data (712 birds from 27 populations) to compute local genetic indices and pairwise genetic distances. Finally, we assessed the relationships between genetic distances or indices and cost distances or connectivity metrics with maximum-likelihood population-effects distance models and Spearman correlations between metrics. Overall, the landscape graphs reliably reflected the influence of connectivity on population genetic structure; validation R² was up to 0.30 and correlation coefficients were up to 0.71. Yet, the relationship among graph ecological relevance, data requirements, and construction and analysis methods was not straightforward because the graph based on the most complex construction method (species distribution modeling) sometimes had less ecological relevance than the others. Cross-validation methods and sensitivity analyzes allowed us to make the advantages and limitations of each construction method spatially explicit. We confirmed the relevance of landscape graphs for conservation modeling but recommend a case-specific consideration of the cost-effectiveness of their construction methods. We hope the replication of independent validation approaches across species and landscapes will strengthen the ecological relevance of connectivity models.     

Density-dependent kinetics models for a simple description of complex phenomena in macroscopic mass-balance modeling of bioreactors

From experiments originally proposed by Ardity et al. [Arditi, R., Perrin, N., Saïah, H., 1991. Functional responses and heterogeneities: an experimental test with cladocerans. Oikos 60, 69–75] in the field of fundamental ecology, this paper derives conditions under which mass balance ratio-dependent kinetics models are more appropriate (in a sense to be specified in the sequel) than substrate-dependent kinetics models to macroscopically describe bioprocesses with attached biomass. From literature results, it is also suggested that the use of Contois growth functions should be preferred to substrate-dependent kinetics when macroscopically modeling an immobilized-biomass bioreactor. The “robustness” of this result, rather based on a qualitative reasoning, is investigated in simulation and a number of fundamental properties of ratio-dependent models are highlighted. Furthermore, it is shown that ratio-dependent models are well suited to describe macroscopically bioreactors where the diffusion phenomenon is obviously one of the most important limiting factor.     

The free energy and information embodied in the amino acid chains of organisms

It is generally accepted as a useful and workable hypothesis that when an ecosystem receives an inflow of exergy (energy that can do work) it will utilize this flow of exergy to move as far away from thermodynamic equilibrium as possible after the exergy (energy) for maintenance has been covered. If more combinations of system components including organisms are offered, the combination of components and processes that will bring the system most away from thermodynamic equilibrium will win.The amino acid sequences of the proteins e.g. enzymes determine and control the life processes of the organisms and may be viewed as information sensu lato. The free energy of oxidation of the amino acids and the peptide bonds of the cell enzymes expresses therefore the exergy content, eco-exergy or work capacity that the information contributes to “moving further away from thermodynamic equilibrium”. In this paper eco-exergy is calculated and plotted versus the β-values (a measure of the information contained in the genome) for different organisms. The eco-exergy density was previously (see [J?rgensen et al., 1995] and [J?rgensen et al., 2005]) proposed to be calculated as the summation of the product of the β-values representing the information of the genome multiplied by the concentrations of the respective ecosystem components. This analysis shows a strong correlation between the β-values and free energy released when oxidizing the enzymes. The β-values can therefore be assumed to represent the free energy that the organisms have invested in genetic information.     

Associations among members of a black-capped chickadee flock

Summary One color-banded black-capped chickadee flock was studied intensively at a winter feeder in Wisconsin. Flock structure was examined by measuring association among flock members in three ways: spatial proximity, temporal association, and association of individuals during flock movements. Additional information was obtained on aggressive interactions at and near the feeder. By midwinter, the strongest attachments were between males and females who subsequently mated; pair attachments strengthened as the breeding season approached. Lower ranked individuals in the dominance hierarchy showed weaker associations with other flock members in early spring than did more dominant individuals. The strong attachment to the mate may have implications for altruistic behavior within the winter flock.     

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.     

Predicting [corrected] extinction risk in spite of predator-prey oscillations.

Most population viability analyses (PVA) assume that the effects of species interactions are subsumed by population-level parameters. We examine how robust five commonly used PVA models are to violations of this assumption. We develop a stochastic, stage-structured predator-prey model and simulate prey population vital rates and abundance. We then use simulated data to parameterize and estimate risk for three demographic models (static projection matrix, stochastic projection matrix, stochastic vital rate matrix) and two time series models (diffusion approximation [DA], corrupted diffusion approximation [CDA]). Model bias is measured as the absolute deviation between estimated and observed quasi-extinction risk. Our results highlight three generalities about the application of single-species models to multi-species conservation problems. First, our collective model results suggest that most single-species PVA models overestimate extinction risk when species interactions cause periodic variation in abundance. Second, the DA model produces the most (conservatively) biased risk forecasts. Finally, the CDA model is the most robust PVA to population cycles caused by species interactions. CDA models produce virtually unbiased and relatively precise risk estimates even when populations cycle strongly. High performance of simple time series models like the CDA owes to their ability to effectively partition stochastic and deterministic sources of variation in population abundance.     

Modelling the spatial structure of forest stands by multivariate point processes with hierarchical interactions

A stochastic model is applied to describe the spatial structure of a forest stand. We aim at quantifying the strength of the competition process between the trees in terms of interaction within and between different size classes of trees using multivariate Gibbs point processes with hierarchical interactions introduced in [Högmander, H., Särkkä, A., 1999. Multitype spatial point patterns with hierarchical interactions. Biometrics 55, 1051–1058]. The new model overcomes the main limitation of the traditional use of the Gibbs models allowing to describe systems with non-symmetric interactions between different objects. When analyzing interactions between neighbouring trees it is natural to assume that the size of a tree determines its hierarchical level: the largest trees are not influenced by any other trees than the trees in the same size class, while trees in the other size classes are influenced by the other trees in the same class as well as by all larger trees. In this paper, we describe a wide range of Gibbs models with both hierarchical and non-hierarchical interactions as well as a simulation algorithm and a parameter estimation procedure for the hierarchical models. We apply the hierarchical interaction model to the analysis of forest data consisting of locations and diameters of tree stems.     

Role of competition in phytoplankton population for the occurrence and control of plankton bloom in the presence of environmental fluctuations

Termination of harmful algal blooms (HABs) and coexistence of phytoplankton–zooplankton populations are of great importance to human health, ecosystem, environment, tourism and fisheries. In this paper, we propose a three component model consisting of non-toxic phytoplankton (NTP), toxin producing phytoplankton (TPP) and zooplankton (Z). The growth of zooplankton species is assume to reduce due to toxic chemicals released by TPP population. We have extended the model proposed by Chattopadhyay et al. [Chattopadhyay, J., Sarkar, R.R., Pal, S., 2004. Mathematical modelling of harmful algal blooms supported by experimental findings. Ecol. Comp. 1, 225–235] by including competition terms between TPP and NTP. We observe the effect of competition factors both in the presence and absence of the environmental fluctuation. From our field as well as model analysis we observe that competition helps in the coexistence of the species, but if the effect of competition is very high on the TPP population, it results in planktonic bloom. It is shown that the coexistence equilibrium loses its stability when the competition coefficient crosses a critical value and resulting Hopf-bifurcation around the positive equilibrium depicting oscillations phenomena of the populations.     

A distance dependent contagion function for vector-based data

Landscape pattern is of primary interest to landscape ecologists and landscape metrics are used to quantify landscape pattern. Metrics are commonly defined and calculated on raster-based land cover maps. One metric is the contagion, existing in several versions, e.g., unconditional and conditional, used as a measure of fragmentation. However, mapped data is sometimes in vector-based format or there may be no mapped data but only a point sample. In this study a definition of contagion for such cases is investigated. The metric is an extension of the usual contagion, based on pairs of points at varying distances and gives a function of the distance. In this study the extended contagion is calculated for vector-based delineated real landscapes and for simulated ones. Both unconditional and conditional contagions are studied using two classification systems. The unconditional contagion function was decreasing and convex, with upper and lower limits highly correlated to the Shannon diversity index, thus carrying only area proportion information. The spatial information lies in the speed by which the function converges to the lower limit; using a proxy function this can be expressed by a single parameter b, with high values for fragmented landscapes. No proxy function was found for the conditional contagion, for which only qualitative information was found. The extended contagion is applicable both in patch mosaic models of landscapes and in gradient-based models, where landscape characteristics change continuously without distinct borders between patches. The extended contagion can be useful in sample based surveys where there no map of the entire landscape is available.     

Anti-predatory vigilance and the limits to collective detection: visual and spatial separation between foragers

Collective detection concerns the idea that all members of a socially feeding group are alerted to an attack as long as at least one group member detects it. We found that collective detection in mixed flocks of emberizid sparrows is limited markedly by relatively small degrees of visual and spatial separation between foragers. These limits on collective detection appear to influence the degree to which flock members lower their vigilance with increasing group size (the group size effect). Specifically, the decrease in collective detection with increasing visual and spatial isolation between foragers is accompanied by a concomitant decrease in the strength of the vigilance group size effect. Explanations for the vigilance-related effects of such separation based upon a bird’s ability to monitor the vigilance behavior of flockmates can be ruled out for our experimental system. Our results also shed light on the issue of whether the vigilance group size effect is influenced more by collective detection or the simple dilution of risk with increasing group size. We argue that collective detection is not only an important determinant of the group size effect, but also that the phenomena of collective detection and risk dilution are interdependent. Received: 25 July 1995/Accepted after revision: 17 December 1995     

Behavioral games involving a clever prey avoiding a clever predator: An individual-based model of dusky dolphins and killer whales

Faced with an intermittent but potent threat, animals exhibit behavior that allows them to balance foraging needs and avoid predators and over time, these behaviors can become hard-wired adaptations with both species trying to maximize their own fitness. In systems where both predator and prey share similar sensory modalities and cognitive abilities, such as with marine mammals, the dynamic nature of predator-prey interactions is poorly understood. The costs and benefits of these anti-predator adaptations need to be evaluated and quantified based on the dynamic engagement of predator and prey. Many theoretic models have addressed the complexity of predator-prey relationships, but few have translated into testable mechanistic models. In this study, we developed a spatially-explicit, geo-referenced, individual-based model of a prototypical adult dusky dolphin off Kaikoura, New Zealand facing a more powerful, yet infrequent predator, the killer whale. We were interested in two primary objectives, (1) to capture the varying behavioral game between a clever prey and clever predator based on our current understanding of the Kaikoura system, (2) to compare evolutionary costs vs. benefits (foraging time and number of predator encounters) for an adult non-maternal dusky dolphin at various levels of killer whale-avoidance behaviors and no avoidance rules. We conducted Monte Carlo simulations to address model performance and parametric uncertainty. Mantel tests revealed an 88% correlation (426 × 426 distance matrix, km²) between observed field sightings of dusky dolphins with model generated sightings for non-maternal adult dusky dolphin groups. Simulation results indicated that dusky dolphins incur a 2.7% loss in feeding time by evolving the anti-predator behavior of moving to and from the feeding grounds. Further, each evolutionary strategy we explored resulted in dolphins incurring an additional loss of foraging time. At low killer whale densities (appearing less than once every 3 days), each evolutionary strategy simulated converged towards the evolutionary cost of foraging, that is, the loss in foraging time approached the 2.7% loss experienced by evolving near shore-offshore movement behavior. However, the highest level of killer whale presence resulted in 38% decreases in foraging time. The biological significance of these losses potentially incurred by a dusky dolphin is dependent on various factors from dolphin group foraging behavior and individual energy needs to dolphin prey availability and behavior.     

Local likelihood disease clustering: development and evaluation

This paper illustrates a method based on local likelihood (LL) for detecting disease clusters. The approach is based on estimating a lasso distance for each region: within which regions are considered to be clustered. An important advantage in implementing this approach is that it does not require any special Monte Carlo Markov Chain (MCMC) algorithm, e.g., reversible jump MCMC, which is essential in hidden Markov model approach. Another advantage is that extending the model to incorporate covariates is straightforward. We illustrate three ways of doing this by using Eastern Germany lip cancer data. By using simulated data, we have made a comparison with the BYM model [Besag et al. (1991) Annals of the Institute of Statistical Mathematics, 43, 1–59] and the mixture model [Lawson and Clark (2002) Disease Mapping and Risk Assessment for Public Health, Chapman and Hall]. We also did a limited examination of the ability of the LL model to recover true relative risk under different priors for lasso parameter. In order to check the edge effects, which has been overlooked in many spatial clustering models for disease mapping but deserves special attention as it lacks observable neighbors, we have adapted here a simple approach to observe the changes in relative risks when the edge regions are omitted. An erratum to this article is available at .     

Can imitation explain dialect origins?

Imitation is one of the central processes underlying learning. Although the mechanisms of imitation at the individual level have received considerable attention, the population effects of imitative behavior have scarcely been investigated. In this paper I address the problem of self-organization at the population level emerging from imitative behavior between individuals. The model considered is a modification of that developed by Durrett and Levin [Durrett, R., Levin, S.A., 2005. Can stable social groups be maintained by homophilous imitation alone? J. Econ. Behav. Organ. 57, 267–286] in investigation of the coexistence of social groups. I modified the previous model in order to approach it in describing not only human societies but also animal populations with simpler cultures. In contrast with the other studies, I do not assume any payoffs related to imitation behavior and the existence of social rank. Individuals are assumed to be of equal rank and to accept opinions of others in proportion to their similarity (homophilous imitation). The symmetrical structure of interactions induces random drift and development of stable self-organized social groups in both homogeneous and spatially distributed societies. This type of self-organization may be widely distributed in natural systems, where imitative behavior takes place. In particular, it can be involved in origins of dialects and ring species.     

Persistence models for mark-recapture

The stable of models available for analyzing mark-recapture data (Otis et al. Wild Momogr 66:135, 1978) includes those having behavioral characteristics, time variation, heterogeneity, along with combinations of those characteristics. This paper proposes use of a series of models based on the persistence model of Ramsey and Usner (Biometrics 59:331–339, 2003). We show that persistence can be modeled in combination with behavior and with time variation. We apply the persistence model to situations in which capture occasions are not equally-spaced in time. Two case studies illustrate the use of these extended persistence models.