Voltage-clamp measurements on single split lamellae of posterior gills of the shore crab Carcinus maenas

Single split lamella preparations of posterior gills of low-salt adapted shore crabs Carcinus maenas (collected from the Kiel Bay, Baltic Sea in 1991) were mounted in a micro Ussing-chamber. With NaCl salines on both sides we found an outside positive potential difference (PD) of 6.6±1.3 mV, a short-circuit current (I_sc) of-240±65 Acm^-2 and a resistance (R_t) of 25±3 cm² (n=8). Substitution of Cl^- (gluconate) on both sides of the preparation resulted in a decrease of I_sc by more than 90% at constant R_t. I_sc disappeared and R_t increased after substitution of Na⁺ (choline). When ouabain (2 mmol l^-1) was applied to the internal NaCl-saline, I_sc decreased and R_t remained unchanged. Internal addition of 0.1 mmol l^-1 acetazolamide left I_sc and R_t unaffected. Application of amiloride to the external NaCl saline resulted in a increase of both inward negative I_sc and R_t. The dose dependence of the diuretic showed a maximal effect between 50 and 200 mol l^-1 with a half-maximal blocker concentration (K_AMI) of ca. 10 mol l^-1. The results show that the split lamella preparation of posterior gills of C. maenas is a low resistance epithelium which is able to effect a massive, electrogenic and coupled absorption of Na⁺ and Cl^-.     

Estimation of some parameters of large fish populations by capture-recapture experiments

A certain number of tagged fish is liberated and assumed to be distributed randomly among a natural fish population. The fish are subjected to a number of fishing experiments within relatively short periods, and lie between equal intervening periods of durationT. Untagged fish are retained, while tagged fish are released during the fishing experiments. Denoting the catchability of untagged fish byq _u and that for tagged fish byq _t , it is assumed that they are related by the equation “q _u =cq _t ” wherec is a constant. Denoting the survival rates of tagged fish and the effective fishing effort of commerical fisheries per unit time from the (k-1)^th to thek ^th experiments by _t S _k andf _k , respectively, it is assumed that they vary from period to period. Assuming that during thek ^th experiment, the number of untagged fish captured and the experimental fishing rate of tagged fish are denoted by _u X _k and _t P _k , respectively, then $$\begin{array}{*{20}c} {\frac{{(_u X_k )^2 }}{{[_u X_{(k - 1)} ][_u X_{(k + 1)} ]}} = \frac{{_t S_k }}{{_t S_{(k + 1)} }} \cdot \frac{{e^{ - (1 - c)q_t f_{(k + 1)} T} }}{{e^{ - (1 - c)q_t f_k T} }} \cdot } \\ {\frac{{1 - c_t P_{(k - 1)} }}{{1 - c_t P_k }} \cdot \frac{{(_t P_k )^2 }}{{[_t P_{(k - 1)} ][_t P_{(k + 1)} ]}}.} \\ \end{array}$$ The above equation containsc as a single unknown, while all other terms are supplied by the capture-recapture experiments, exceptf _k andf _(k+1) which may be obtained from fisheries statistics. A number of the above equations are obtained from several experiments and can be combined into a single equation to obtain an overall estimate forc which can be used to derive estimates for experimental fishing rates, abundance, and instantaneous natural and fishing mortality rates for natural fish populations. These estimates are free from type (A) tagging errors, and have the advantage of taking into consideration the probable different behaviour of tagged and untagged fish.     

Parameter fitting in dynamic models

We consider the numerical approaches for the least squares estimation of the parameter vector p in the initial value problem y′ = g(t, y, p), y(t₀) = y₀(p) when observations are available on some or all components of the vector y(t). Special attention is paid to the development of techniques which, although not global, are less sensitive to initial parameter estimates than the standard approach employing the sensitivity equations. Experience indicates that interactive approaches can be very valuable when good starting parameter approximations are unavailable. We describe the main features of our interactive parameter fitting package PARFIT. This package contains standard techniques employing the sensitivity equations as well as special algorithms designed to improve poor parameter estimates. These special algorithms have been selected and developed with user interaction in mind. We describe in detail one special approach designed for the case when observations are not available on all state variables. An example (using computer generated observations) is presented to illustrate this approach. Finally, the power of an interactive approach is demonstrated with two examples involving attempts to model physically observed phenomena.     

Spatial pattern and persistence of historical fire boundaries in southern interior British Columbia

Recent ecosystem and fire management research aims to quantify, model and understand historical fire disturbances focusing on their frequency, size and distribution. Less attention, however, has been paid to fire boundaries and their location on the landscape. Our study presents a spatial method to quantify the location, pattern and persistence of historical fire boundaries using tree ring fire scar data in the lower Stein watershed (British Columbia). Data from Pinus ponderosa and Pseudotsuga menziesii collected in 35 one-hectare plots over a 412-hectare study area were analyzed for the period between 1879 and 1947 using local spatial statistics and boundary detection techniques. Results of the analysis using local spatial statistic Moran’s I showed significant clustering of boundaries near topographic breaks. To determine and test whether fire boundaries between plots were persistent, we used boundary detection methods and developed a spatially restricted randomization test. The results revealed that out of 86 possible boundary links, 8 were significantly persistent (P < 0.025) while another 8 were significantly rare (P < 0.025). These spatial methods can help determine the historical spatial configuration of persistent boundaries and can be used to maintain natural forest dynamics.

The theoretical basis for estimating phytoplankton production and specific growth rate from chlorophyll, light and temperature data

Phytoplankton maximum specific growth rate, μ_max, and maximum photosynthetic quantum yield, Φ_max, can be related mathematically via the photosynthetic light curve, P(I). A model is presented in which maximum quantum yield defines the initial slope of the light curve and is assumed to be a known constant, while maximum specific growth rate defines the light-saturated region of the curve and is assumed to be a known function of temperature. The effect of introducing μ_max(T) into P(I, Φ_max) is to replace the unknown, temperature-dependent light saturation parameter with a term involving the ratio μ_max/Φ_max. The advantage of writing P(I) in terms of both μ_max and Φ_max is that those parameters are particularly well documented in the literature. Consequently, estimates of nutrient-unlimited phytoplankton growth and production rates can be based solely on the constants μ_max, Φ_max and k_c (light absorption per unit of chlorophyll) and the free variables light, temperature and chlorophyll concentration. Rate estimates appear to be accurate to within a factor of two for an extremely wide range of conditions.One particularly significant result of introducing μ_max into P(I, Φ_max) is that the carbon : chlorophyll ration, θ, appears explicitly. It is possible to derive an expression for optimum θ based on the assumption that adaptive changes in carbon/chlorophyll occur so as to maximize the specific growth rate for given conditions of light and temperature. Laboratory and field data are compiled from the literature to test the formulae presented here.     

Matching species traits to environmental variables: a new three-table ordination method

This paper addresses the question of studying the joint structure of three data tablesR,L andQ. In our motivating ecological example, the central tableL is a sites-by-species table that contains the number of organisms of a set of species that occurs at a set of sites. At the margins ofL are the sites-by-environment data tableR and the species-by-trait data table Q. For relating the biological traits of organisms to the characteristics of the environment in which they live, we propose a statistical technique calledRLQ analysis (R-mode linked toQ-mode), which consists in the general singular value decomposition of the triplet (R ^t D _I LD _J Q,D _q ,D _p ) whereD _I ,D _J ,D _q ,D _p are diagonal weight matrices, which are chosen in relation to the type of data that is being analyzed (quantitative, qualitative, etc.). In the special case where the central table is analysed by correspondence analysis,RLQ maximizes the covariance between linear combinations of columns ofR andQ. An example in bird ecology illustrates the potential of this method for community ecologists.     

Synthesizing units as modeling tool for photosynthesizing organisms with photoinhibition and nutrient limitation

This paper develops a mechanistic ecological model of photosynthesis based on a synthesizing unit with terms for nutrient limitation and wavelength dependent photoinhibition. The model satisfactorily fits standard P-I data with a clear inhibitory impact of excess radiation. Furthermore, the model satisfactorily discriminates between the inhibitory impact of PAR, UVA and UVB. The inhibitory potential of UVA is less than 1 order of magnitude higher than that of PAR, whereas that of UVB is about 2 orders of magnitude higher than PAR. The model can also satisfactorily describe photosynthesis rates as a function of both PAR and environmental nitrate concentrations. At relatively low nutrient levels, the model produces a curve that ascends quickly to a near saturation level; this is a trend often observed in experimental data and described well by a hyperbolic tangent. The results in this paper suggest that nutrient limitation is an overlooked factor in the experimental design for obtaining P-I data. The SU model in this paper is the simplest possible model of photosynthesis with nutrient limitation and photoinhibition that is consistent with Dynamic Energy Budget theory, in which the model can be embedded to obtain dynamic properties.     

Use of pulse amplitude modulated (PAM) fluorometry for in situ measurements of photosynthesis in two Red Sea faviid corals

Measurements of the photosynthetic activity of symbiotic zooxanthellae in corals under natural growth conditions has been limited until recently, and this is one of the first reports on utilising a newly developed underwater pulse amplitude modulated (PAM) fluorometer (the Diving-PAM, Walz Gmbh, Germany) for such studies in situ. Photosynthetic responses to irradiance (photosynthetic photon flux, PPF) of the two faviid corals Favia favus (Forskål) and Platygyra lamellina (Ehrenberg) were measured while snorkelling or SCUBA diving (in August 1997), and we report here the results in terms of effective quantum yields of photosystem II (Y?) and estimated rates of photosynthetic electron transport (ETR, calculated as Y?×?0.5?× PPF?×?FA, where FA is the estimated fraction of light absorbed by the photosymbiont-containing tissue). Both species showed a reduction in Y with increasing actinic irradiances produced by the instrument above 500?μmol photons m^?2 s^?1, and the corresponding ETR values yielded apparently typical photosynthesis versus irradiance (P-I?) curves, which saturated between 1500 and 2000?μmol photons m^?2 s^?1. It was found that 30?s irradiation at each PPF level was sufficient to give optimal ETR values and, therefore, each P-I curve could be obtained within a few minutes. In situ point measurements from various areas of colonies under ambient light showed average ETR values within the range expected from the P-I curves. In order to test the Diving-PAM in an eco-physiologically relevant experiment, photosynthetic ETR versus PPF was measured for three sections of a large P. lamellina, each section of which received different natural irradiance levels. The results clearly demonstrated adaptations to the ambient light field in that vertical and downward-facing portions of the colony showed gradually lower maximal ETRs, steeper initial slopes of the P-I curves and, accordingly, lower light saturation points than upward-facing areas receiving higher light levels. Based on these trials, some evaluations are given as to the applicability of the Diving-PAM for photosynthetic measurements when monitoring similar corals.     

A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions

The evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (I_R) were discussed according to the models’ features and to the typology and number of processes simulated. I_R increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between I_R and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation.     

Locating major PM10 source areas in Seoul using multivariate receptor modeling

Identifying the major sources contributing to air pollution is a problem of fundamental importance in developing effective air quality management plans. Multivariate receptor modeling aims to achieve this goal by unfolding the air pollution data into components associated with different sources based on factor analysis models. We analyze the PM₁₀ data obtained from 17 monitoring sites in Seoul to locate the major source regions using multivariate receptor modeling. The model uncertainty caused by the unknown number of sources and identifiability conditions is assessed by posterior probability of each model. The estimated source spatial profiles seem to be consistent with our prior expectation about the PM₁₀ sources in Seoul.     

Long-run changes in radiative forcing and surface temperature: The effect of human activity over the last five centuries

We test two hypotheses that are derived from the anthropogenic theory of climate change. The first postulates that a growing population and increasing economic activity increase anthropogenic emissions of radiatively active gases relative to natural sources and sinks, and this alters global biogeochemical cycles in a way that increases the persistence of radiative forcing and temperature. The second postulates that the increase in the persistence of radiative forcing transmits a stochastic trend to the time series for temperature. Results indicate that the persistence of radiative forcing and temperature changes from I(0) to I(1) during the last 500 years and that the I(1) fingerprint in radiative forcing can be detected in a statistically measureable fashion in surface temperature. As such, our results are consistent with the physical mechanisms that underlie the theory of anthropogenic climate change.     

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.     

A spatially explicit model to predict future landscape composition of aspen woodlands under various management scenarios

Quaking aspen (Populus tremuloides) is declining across the western United States. Aspen habitats are among the most diverse plant communities in this region and loss of these habitats can result in shifts in biodiversity, productivity, and hydrology across a range of spatial scales. Western aspen occurs on the majority of sites seral to conifer species, and long-term maintenance of these aspen woodlands requires periodic fire. Over the past century, fire intervals, extents, and intensities have been insufficient to regenerate aspen stands at historic rates; however the effects of various fire regimes and management scenarios on aspen vegetation dynamics at broad spatial and temporal scales are unexplored. Here we use field data, remotely sensed data, and fire atlas information to develop a spatially explicit landscape simulation model to assess the effects of current and historic wildfire regimes and prescribed burning programs on landscape vegetation composition across two mountain ranges in the Owyhee Plateau, Idaho. Model outputs depict the future structural makeup and species composition of the landscape at selected time steps under simulated management scenarios. We found that under current fire regimes and in the absence of management activities, loss of seral aspen stands will continue to occur over the next two centuries. However, a return to historic fire regimes (burning 12–14% of the modeled landscape per decade) would maintain the majority of aspen stands in early and mid seral woodland stages and minimizes the loss of aspen. A fire rotation of 70–80 years was estimated for the historic fire regime while the current fire regime resulted in a fire rotation of 340–450 years, underscoring the fact that fire is currently lacking in the system. Implementation of prescribed burning programs, treating aspen and young conifer woodlands according to historic fire occurrence probabilities, are predicted to prevent conifer dominance and loss of aspen stands.     

The Reliability of Using Population Viability Analysis for Risk Classification of Species

I examine whether or not it is appropriate to use extinction probabilities generated by population viability analyses, based on best estimates for model parameters, as criteria for listing species in Red Data Book categories as recently proposed by the World Conservation Union. Such extinction probabilities are influenced by how accurately model parameters are estimated and by how accurately the models depict actual population dynamics. I evaluate the effect of uncertainty in parameter estimation through simulations. Simulations based on Steller sea lions were used to evaluate bias and precision in estimates of probability of extinction and to consider the performance of two proposed classification schemes. Extinction time estimates were biased (because of violation of the assumption of stable age distribution) and underestimated the variability of probability of extinction for a given time (primarily because of uncertainty in parameter estimation). Bias and precision in extinction probabilities are important when these probabilities are used to compare the risk of extinction between species. Suggestions are given for population viability analysis techniques that incorporate parameter uncertainty. I conclude that testing classification schemes with simulations using quantitative performance objectives should precede adoption of quantitative listing criteria.     

Understanding Interaction Effects of Climate Change and Fire Management on Bird Distributions through Combined Process and Habitat Models

Abstract: Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process‐based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail (Callipepla squamata), Loggerhead Shrike (Lanius ludovicianus), and Rock Wren (Salpinctes obsoletus), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo (Fouquieria splendens) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf‐area index values were lower in shrubland. This high probability of occurrence likely is related to the species’ use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.     

Analysis of modified model for commercial fishing with possible extinctive fishery resources

The solution properties of a modified commercial fishing model are investigated in this paper. The results imply that fishery resources may be extincted under certain conditions. The positive invariant region, that is, the nonextinctive region, R(δ₁, δ₂), is a monotonic increasing correspondence of exiting rate of the fishing industry, δ₂, and is a monotonic decreasing correspondence of the entering rate of the fishing industry, δ₁. Also, there exist limit cycles for the system of equations of the modified model under certain conditions.     

Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty

Fire managers need to study fire history in terms of occurrence in order to understand and model the spatial distribution of the causes of ignition. Fire atlases are useful open sources of information, recording each single fire event by means of its geographical position. In such cases the fire event is considered as point-based, rather than area-based data, completely losing its surface nature. Thus, an accurate method is needed to estimate continuous density surfaces from ignition points where location is affected by a certain degree of uncertainty. Recently, the fire scientific community has focused its attention on the kernel density interpolation technique in order to convert point-based data into continuous surface or surface-data. The kernel density technique needs a priori setting of smoothing parameters, such as the bandwidth size. Up to now, the bandwidth size was often based on subjective choices still needing expert knowledge, eventually supported by empirical decisions, thus leading to serious uncertainties. Nonetheless, a geostatistical model able to describe the point concentration and consequently the clustering degree is required. This paper tries to solve such issues by implementing the kernel density adaptive mode. Lightning/human-caused fires occurrence was investigated in the region of Aragón's autonomy over 19 years (1983–2001) using 3428 and 4195 ignition points respectively for the two causes of fire origin. An analytical calibration procedure was implemented to select the most reliable density surfaces to reduce under/over-density estimation, overcoming the current drawbacks to define it by visual inspection or personal interpretation. Besides, ignition point location uncertainty was investigated to check the sensitivity of the proposed model. The different concentration degree and the dissimilar spatial pattern of the two datasets, allow testing the proposed calibration methodology under several conditions. After having discovered the slight sensitivity of the model to the exact point position, the obtained density surfaces for the two causes were combined to discover hotspot areas and spatial patterns of the two causes. Evident differences in spatial location of the origin causes were noted and described. The general trend follows the geographical features and the human activity of the study areas. The proposed technique should be promising to support decision-making in wildfire prevention actions, because of the occurrence map can be used as a response variable in fire risk predicting models.