A coffee agroecosystem model: I. Growth and development of the coffee plant

This paper is the first of three on the coffee production system consisting of (1) the coffee plant, (2) coffee berry borer (CBB) and (3) the role of CBB parasitoids. A previous simulation model of the coffee plant was developed using data from Brazil where coffee phenology is characterized by distinct seasonal flowering (Gutierrez et al., 1998). In contrast, flowering in Colombia is continuous with low seasonality. To capture the differences in coffee phenology and growth in the two climatic regions, the Gutierrez et al. (1998) model was modified using new data from Colombia.The modifications to the model include:

(1)

The effect of solar radiation on floral buds initiation;

(2)

An age structure population model to track the daily input and development of the floral buds;

(3)

The effect of leaf water potential on breaking dormancy in flower buds, and hence on the timing and intensity of flowering;

(4)

The incorporation of both the vegetative and the reproductive demands to predict the photosynthetic rate.

(5)

The effect of low temperature on photosynthesis and defoliation.

Other aspects of the model were re-interpreted and refinements made to generalize its structure for use across coffee varieties and geographic areas. The model, without modification, realistically simulates field data from Brazil and two Colombian locations having different varieties, patterns of rainfall and hence flowering phenology.The model will be used as the base trophic level for incorporating CBB and high tropic levels effects, and for the analysis of management options in the coffee production system.     

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.     

Resistance and re-organization of an ecosystem in response to biological invasion: Some hypotheses

Using a dynamic model of Lake Chozas developed by Marchi et al. (2011), we tested three hypotheses about recovery of the indigenous community and water quality after radical changes caused by introduction of an invasive allochthonous crayfish, Procambarus clarkii:

1.

Can the lake resist the pressure of an invasive species, like P. clarkii, by adaptation?

2.

Can the ecosystem recover when all the crayfish are removed and low phosphorus concentrations persist in inflow water?

3.

Does the simulated recovery of submerged vegetation occur at a total phosphorus concentration below 100 mg TP m⁻³, as estimated by Scheffer et al. (1993), Scheffer (1997), Jeppesen et al. (1998) and Zhang et al. (2003)?

We obtained the following answers:

1.

Lake Chozas can at least partly resist by adaptation. A combination of possible parameter changes could lead to a significant increase in eco-exergy.

2.

Removal of the phosphorus represented by crayfish (by harvesting) implies complete recovery of the lake and its eco-exergy, albeit not necessarily with the same organisms having the same properties.

3.

The expected hysteresis created by introduction and harvesting of crayfish is observed under the following conditions: phytoplankton dominance at total phosphorus ≥ about 200-250 mg TP m⁻³ and submerged vegetation returns at total phosphorus < 100 mg TP m⁻³.

     

Modelling radiative balance in a row-crop canopy: Row-soil surface net radiation partition

Row crops like vineyards undergo various and significant manipulations of training system and cultural practices, which strongly influence the quality of products. Variations of water vapour fluxes from the soil surface and the leaves in the row volume are closely linked to the ratio of energy available to each compartment. A physically realistic model of available energy partition between the rows and the soil surface is therefore a key factor towards optimization of such systems, and must be included in canopy models. A number of available models were not directly validated. The purpose of the study was therefore to design a model of net radiation partition and check it directly.The model of net radiation partition between rows (Rnv), considered as a whole, and intervening soil surface (Rns) of a row-crop canopy was developed from physically realistic yet simple assumptions:

-

global solar (short wave) radiation partition was calculated by a previously validated geometric model;

-

long-wave radiative fluxes between the soil surface, the rows and the atmosphere were calculated from the corresponding view factors, which only depended on canopy geometry;

-

atmospheric radiation was estimated by a simple empirical relation based on air temperature as the only input variable;

-

air temperature in the vicinity of leaves replaced leaf surface temperatures as a more convenient input variable, with little loss of information.

The input variables were incoming direct and diffuse solar radiation, soil surface mean temperature and air temperature near the leaves. The main parameters were soil and leaf albedos, row porosity and dimensions.A direct validation of the model was attempted by measuring net radiation above the canopy and at five positions above the soil surface in a vineyard of the Bordeaux area. The reliability of soil surface net radiation measurements was estimated by thorough error propagation analysis. When found significant, errors were corrected and finally soil surface net radiation data were corrected only for delay in direct downward solar radiation striking net radiometers, because canopy was discontinuous and height of net radiometers was not negligible compared to canopy height.In these conditions, model calculations were in agreement with measurements, although the model slightly underestimated Rns and therefore overestimated Rnv. As the mean error was about 20 W m⁻², and therefore compatible with instrument accuracy, the results were considered satisfactory.This available energy partition model is able to estimate radiative balance in various canopy systems and in various thermal environment conditions, leading to easier simulations of energy balance and water fluxes. It could therefore be a useful tool for optimizing row-crop canopies, taking fully into account any kind of present or future thermal environment.     

Definition and calculation of uncertainty in ecological process models

We present a method of multi-criteria assessment for the analysis of process model uncertainty that combines analysis of model structure, parameters and data requirements. There are three components in calculation and definition of uncertainty.

(1)

Assessment criteria: Uncertainty in a process model is reduced as the model can simultaneously simulate an increased number of assessment criteria selected to test specific aspects of the theory being investigated, and within acceptable limits set for those criteria. This reduces incomplete specification of the model—the characteristic that a model may explain some, but not all, of the observed features of a phenomenon. The calculation required is computation of the Pareto set which provides the list of simultaneously achieved criteria within specified ranges.     

A spatial clustering perspective on autocorrelation and regionalization

We revisit one of the classical problems in geography and cartography where multiple observations on a lattice (N) need to be grouped into many fewer regions (G), especially when this number of desired regions is unknown a priori. Since an optimization through all possible aggregations is not feasible, a hierarchical classification scheme is proposed with an objective function sensitive to spatial pattern. The objective function to be minimized during the assignment of observations to regions (classification) consists of two terms: the first characterizes accuracy and the second, model complexity. For the latter, we introduce a spatial measure that characterizes the number of homogeneous patches rather than the usual number of classes. A simulation study shows that such a classification procedure is less sensitive to random and spatially correlated error (noise) than non-spatial classification. We also show that for conditional autoregressive error (noise) fields the optimal partitioning is the one that has the highest within-units generalized Moran coefficient. The classifier is implemented in ArcView to demonstrate both a socio-economic and an environmental application to illustrate some potential applications.

Spatial structure effects on the detection of patches boundaries using local operators

Landscapes exhibit various degrees of spatial heterogeneity according to the differential intensity and interactions among processes and disturbances that they are subjected to. The management of these spatially dynamical landscapes requires that we can accurately map them and monitor the evolution of their spatial arrangement through time. Such a mapping requires first the delineation of various spatial features present in the landscape such as patches and their boundaries. However, there are several environmental (spatial variability) as well as technical (spatial resolution) factors that impair our ability to accurately delineate patches and their boundaries as polygons. Here, we investigate how the spatial structure and spatial resolution of the data affect the accuracy of detecting patches and their boundaries over simulated landscapes and real data. Simulated landscapes consisted of two patches with parameterized spatial properties (patches’ level of spatial autocorrelation, mean value and variance) separated by a boundary of known location. Real data allowed the investigation of a more complex landscape where there is a known transition between two forest domains with unknown spatial properties. Boundary locations are defined using the lattice-wombling edge detector at various aggregation levels and the degree of patch homogeneity is determined using Getis-Ord’s G*. Results show that boundary detection using a local edge detector is greatly affected by the spatial conditions of the data, namely variance, abruptness of the spatial gradient between two patches and patches’ level of spatial autocorrelation. They also suggest that data aggregation is not a panacea for bringing out the ecological process creating the patches and that indicators derived from local measures of spatial association can be complementary tools for analysing spatial structures affecting boundary delineation.

Partitioning of \alpha and \beta diversity using hierarchical Bayesian modeling of species distribution and abundance

Diversity partitioning is becoming widely used to decompose the total number of species recorded in an area or region \((\gamma )\) into the average number of species within samples \((\alpha )\) and the average difference in species composition \((\beta )\) among samples. Single-value metrics of \(\alpha \) and \(\beta \) diversity are popular because they may be applied at multiple scales and because of their ease in computation and interpretation. Studies thus far, however, have emphasized observed diversity components or comparisons to randomized, null distributions. In addition, prediction of \(\alpha \) and \(\beta \) components using environmental or spatial variables has been limited to more extensive data sets because multiple samples are required to estimate single \(\alpha \) and \(\beta \) components. Lastly, observed diversity components do not incorporate variation in detection probabilities among species or samples. In this study, we used hierarchical Bayesian models of species abundances to provide predictions of \(\alpha \) and \(\beta \) components in species richness and composition using environmental and spatial variables. We illustrate our approach using butterfly data collected from 26 grassland remnants to predict spatially nested patterns of \(\alpha \) and \(\beta \) based on the predicted counts of butterflies. Diversity partitioning using a Bayesian hierarchical model incorporated variation in detection probabilities by butterfly species and habitat patches, and provided prediction intervals for \(\alpha \) and \(\beta \) components using environmental and spatial variables.     

Insights on plant interaction between dominating species from patterns of plant association: expected covariance of pin-point cover measurements of two species

It has been suggested that in order to infer ecological processes from observed patterns of species abundance we need to investigate the covariance in species abundance. Consequently, an expression for the expected covariance of pin-point cover measurements of two species is developed. By comparing the observed covariance with the expected covariance we get a new type of information on the spatial arrangement of two species. Here the discrepancy between the observed and expected covariance may be thought of as a measure of the spatial configuration of the two species that may indicate underling ecological processes. The method is applied in a case study of Calluna vulgaris and Deschampsia flexuosa on dry heathland sites. The observed covariance of Calluna and Deschampsia at the level of the sites was positively and significantly correlated with the expected covariance. Negative covariance was observed on sites where both Calluna and Deschampsia had a high cover, which is in agreement with the notion that both species form distinct patches. Oppositely, at sites where both species have a low cover, we found that both the expected and observed covariance were positive. The proposed measure for the expected covariance of two species does capture information on the combined spatial configuration of the two species if the species are common. We show how this may be relevant for understanding the underlying ecological processes leading to the observed covariance.     

Simulation of herbicide impacts on a plant community: comparing model predictions of the plant community model IBC-grass to empirical data

Background

Semi-natural plant communities such as field boundaries play an important ecological role in agricultural landscapes, e.g., provision of refuge for plant and other species, food web support or habitat connectivity. To prevent undesired effects of herbicide applications on these communities and their structure, the registration and application are regulated by risk assessment schemes in many industrialized countries. Standardized individual-level greenhouse experiments are conducted on a selection of crop and wild plant species to characterize the effects of herbicide loads potentially reaching off-field areas on non-target plants. Uncertainties regarding the protectiveness of such approaches to risk assessment might be addressed by assessment factors that are often under discussion. As an alternative approach, plant community models can be used to predict potential effects on plant communities of interest based on extrapolation of the individual-level effects measured in the standardized greenhouse experiments. In this study, we analyzed the reliability and adequacy of the plant community model IBC-grass (individual-based plant community model for grasslands) by comparing model predictions with empirically measured effects at the plant community level.

Results

We showed that the effects predicted by the model IBC-grass were in accordance with the empirical data. Based on the species-specific dose responses (calculated from empirical effects in monocultures measured 4 weeks after application), the model was able to realistically predict short-term herbicide impacts on communities when compared to empirical data.

Conclusion

The results presented in this study demonstrate an approach how the current standard greenhouse experiments—measuring herbicide impacts on individual-level—can be coupled with the model IBC-grass to estimate effects on plant community level. In this way, it can be used as a tool in ecological risk assessment.

     

Fine-scale genetic structure in the surface brooding Caribbean octocoral,Antillogorgia elisabethae

Larval dispersal is a critical component of marine species’ life histories because it controls their population dynamics. Dispersal distance can be inferred by the presence and scale of spatial genetic structure (SGS). The larvae of Antillogorgia elisabethae, a surface brooding Caribbean octocoral, have been observed to settle within meters of maternal colonies, although many disperse over greater distances. Using a spatially hierarchical sampling design, A. elisabethae recruits were collected from a site off the coast of Great Abaco, The Bahamas, in June of 2009–2011. Seven microsatellite loci were used to determine whether genetic structure was present among recruits on scales of <1 m to 10s of meters. Multilocus genotypes were autocorrelated within spatial distance classes using Moran’s I coefficient as an estimate of genetic similarity, and Rousset’s genetic distance (â). Genetic structure was present on a scale of <10 m in 2010, but not in the other 2 years, suggesting that larval dispersal was more localized in 2010. Moran’s I coefficients were positive and significant for 0–1, 1–2 and 2–5 m classes in 2010, and the average genetic distance (â) among recruits in the 0–1 m class was significantly lower than the other classes. Autocorrelograms suggested that genetic patch sizes were 7.5 m in 2010, and 20 m in 2009 and 2011. Differences in the SGS of recruits between years might reflect temporal variation in temperature, wind and/or current speeds affecting larval dispersal, or variation in reproduction at the site.     

Investigating the complex relationship between in situ Southern Ocean pCO_{2} and its ocean physics and biogeochemical drivers using a nonparametric regression approach

The objective in this paper is to investigate the use of a non-parametric approach to model the relationship between oceanic carbon dioxide \((pCO_2)\) and a range of ocean physics and biogeochemical in situ variables in the Southern Ocean, which influence its in situ variability. The need for this stems from the need to obtain reliable estimates of carbon dioxide concentrations in the Southern Ocean which plays an important role in the global carbon flux cycle. The main challenge involved in this objective is the spatial limitation and seasonal bias of the in situ data. Moreover, studies have also reported that the relationship between \(pCO_2\) and its drivers is complex. As such, in this paper, we use the non-parametric kernel regression approach since it is able to accurately capture the complex relationships between the response and predictor variables. In this analysis we use the in situ data obtained from the SANAE49 return leg journey between Antarctic to Cape Town. To the best of our knowledge, this is the first time this data set has been subjected to such analysis. The model variants were developed on a training data subset, and the ‘goodness’ of the models were assessed on an “unseen” test data subset. Results indicate that the nonparametric approach consistently captures the relationship more accurately in terms of mean square error, root mean square error and mean absolute error, over a standard parametric approach (multiple linear regression). These results provide a platform for using the developed nonparametric regression model based on in situ measurements to predict \(pCO_2\) for a larger spatial region in the Southern Ocean based on satellite biogeochemical measurements of predictor variables, given that satellites do not measure \(pCO_2\) .     

Designing environmental monitoring networks to measure extremes

This paper discusses challenges arising in the design of networks for monitoring extreme values over the domain of a random environmental space-time field {X _ij } i = 1, . . . , I denoting site and j = 1, . . . denoting time (e.g. hour). The field of extremes for time span r over site domain i = 1, . . . ,I is given by \(\{Y_{i(r+1)}=\max_{j=k}^{k+n-1} X_{ij}\}\) for k = 1 + rn, r = 0, . . . ,. Such networks must not only measure extremes at the monitored sites but also enable their prediction at the non-monitored ones. Designing such a network poses special challenges that do not seem to have been generally recognized. One of these problems is the loss of spatial dependence between site responses in going from the environmental process to the field of extremes it generates. In particular we show empirically that the intersite covariance Cov(Y _i(r+1),Y _i′(r+1)) can generally decline toward zero as r increases, for site pairs i ≠ i′. Thus the measured extreme values may not predict the unmeasured ones very precisely. Consequently high levels of pollution exposure of a sensitive group (e.g. school children) located between monitored sites may be overlooked. This potential deficiency raises concerns about the adequacy of air pollution monitoring networks whose primary role is the detection of noncompliance with air quality standards based on extremes designed to protect human health. The need to monitor for noncompliance and thereby protect human health, points to other issues. How well do networks designed to monitor the field monitor their fields of extremes? What criterion should be used to select prospective monitoring sites when setting up or adding to a network? As the paper demonstrates by assessing an existing network, the answer to the first question is not well, at least in the case considered. To the second, the paper suggests a variety of plausible answers but shows through a simulation study, that they can lead to different optimum designs. The paper offers an approach that circumvents the dilemma posed by the answer to the second question. That approach models the field of extremes (suitably transformed) by a multivariate Gaussian-Inverse Wishart hierarchical Bayesian distribution. The adequacy of this model is empirically assessed in an application by finding the relative coverage frequency of the predictive credibility ellipsoid implied by its posterior distribution. The favorable results obtained suggest this posterior adequately describes that (transformed) field. Hence it can form the basis for designing an appropriate network. Its use is demonstrated by a hypothetical extension of an existing monitoring network. That foundation in turn enables a network to be designed of sufficient density (relative to cost) to serve its regulatory purpose.     

Hierarchical spatial point process analysis for a plant community with high biodiversity

A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species’ interactions and to generate new biological questions and hypotheses on species’ interactions.

The effect of export to the deep sea on the long-range transport potential of persistent organic pollutants

Background

Export to the deep sea has been found to be a relevant pathway for highly hydrophobic chemicals. The objective of this study is to investigate the influence of this process on the potential for long-range transport (LRT) of such chemicals.

Methods

The spatial range as a measure of potential for LRT is calculated for seven PCB congeners with the multimedia fate and transport model ChemRange. Spatial ranges for cases with and without deep sea export are compared.

Results and Discussion

Export to the deep sea leads to increased transfer from the air to the surface ocean and, thereby, to lower spatial ranges for PCB congeners whose net deposition rate constant is similar to or greater than the atmospheric degradation rate constant. This is fulfilled for the PCB congeners 101, 153, 180, and 194. The spatial ranges of the congeners 8, 28, and 52, in contrast, are not affected by deep sea export. With export to the deep sea included in the model, the spatial ranges of the heavier congener are similar to those of the lighter ones, while the intermediate congeners 101 and 153 have the highest potential for long-range transport.

Conclusions

Transfer to the deep ocean affects the mass balance and the potential for LRT of highly hydrophobic chemicals and should be included in multimedia fate models containing a compartment for ocean water.     

Gravity currents with double stratification: a numerical and analytical investigation

We consider high-Reynolds-number Boussinesq gravity current and intrusion systems in which both the ambient and the propagating “current” are linearly stratified. The main focus is on a current of fixed volume released from a rectangular lock; the height ratio of the fluids $H$ , the stratification parameter of the ambient $S$ , and the internal stratification parameter of the current, $\sigma $ , are quite general. We perform two-dimensional Navier–Stokes simulation and compare the results with those of a previously-published one-layer shallow-water model. The results provide insights into the behavior of the system and enhance the confidence in the approximate model while also revealing its limitations. The qualitative predictions of the model are confirmed, in particular: (1) there is an initial “slumping” stage of propagation with constant speed $u_N$ , after which $u_N$ decays with time; (2) for fixed $H$ and $S$ , the increase of $\sigma $ causes a slower propagation of the current; (3) for some combinations of the parameters $H,S, \sigma $ the fluid released from the lock lacks initially (or runs out quickly of) buoyancy “driving power” in the horizontal direction, and does not propagate like a gravity current. There is also a fair quantitative agreement between the predictions of the model and the simulations concerning the spread of the current.     

Variation in fertilisation abilities between hemiclonal hybrid and sexual parental males of sympatric water frogs (<Emphasis Type="Italic">Rana lessonae</Emphasis>,<Emphasis Type="Italic"> R. esculenta</Emphasis>,<Emphasis Type="Italic"> R. ridibunda</Emphasis>)

In many species, males and females mate with multiple partners, which gives rise to sperm competition and multiple paternity. The experiments on water frogs presented here demonstrate that such sperm competition can affect the structure and dynamics of mixed-species communities. The hybrid frog Rana esculenta (LR) mates with one of its parental species, usually R. lessonae (LL), although in some areas R. ridibunda (RR), to regain the premeiotically eliminated parental genome ("hybridogenesis"). Mixed LL/LR-populations are stable although hybrid numbers should continuously increase at the expense of parental animals, because of differences in female fecundity and other factors. This would finally lead to the extinction of the sexual host, followed by that of the sexual parasite, unless the reproductive superiority of R. esculenta is reduced by other factors, such as lower hybrid male fertility. Eggs from LL- and LR-females were fertilised in vitro by single- and multi-male sperm suspensions of LL-, LR- and RR-males. In all experiments, the proportion of offspring sired by R. esculenta sperm was significantly lower than that sired by R. lessonae or R. ridibunda sperm. Gonad mass, sperm morphology, sperm swimming velocity, and sperm survival did not explain these differences in fertilisation success; nor did gamete recognition and compatibility. Sperm density was the only trait that paralleled fertilisation success, but it offers no explanation either, because densities were equalised for the in-vitro fertilisations. In natural LL/LR populations, the significantly smaller amount, poorer competitive ability and lower long-term survival of R. esculenta compared to R. lessonae sperm will reduce the initial reproductive superiority of hybrids and contribute to the stabilisation of mixed water-frog populations. Differences in fertilisation ability are also likely to be relevant for the structure and dynamics of several other systems with encounters between eggs and sperm from different genotypes, ecotypes, ploidy levels and/or species.