The equilibrium assumption in estimating the parameters of metapopulation models

1.  The construction of a predictive metapopulation model includes three steps: the choice of factors affecting metapopulation dynamics, the choice of model structure, and finally parameter estimation and model testing.
2.  Unless the assumption is made that the metapopulation is at stochastic quasi-equilibrium and unless the method of parameter estimation of model parameters uses that assumption, estimates from a limited amount of data will usually predict a trend in metapopulation size.
3.  This implicit estimation of a trend occurs because extinction-colonization stochasticity, possibly amplified by regional stochasticity, leads to unequal numbers of observed extinction and colonization events during a short study period.
4.  Metapopulation models, such as those based on the logistic regression model, that rely on observed population turnover events in parameter estimation are sensitive to the implicit estimation of a trend.
5.  A new parameter estimation method, based on Monte Carlo inference for statistically implicit models, allows an explicit decision about whether metapopulation quasi-stability is assumed or not.
6. Our confidence in metapopulation model parameter estimates that have been produced from only a few years of data is decreased by the need to know before parameter estimation whether the metapopulation is in quasi-stable state or not.
7. The choice of whether metapopulation stability is assumed or not in parameter estimation should be done consciously. Typical data sets cover only a few years and rarely allow a statistical test of a possible trend. While making the decision about stability one should consider any information about the landscape history and species and metapopulation characteristics.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Use chemometric techniques in the optimization of a specific bioassay for betalactams in milk

Aims: A microbiological bioassay using Geoacillus stearothermophilus was optimized to detect betalactams at concentrations near to the Maximum Residue Limits (MRLs), with low cross‐specificity for tetracycline. Methods and Results: A factorial design (3 × 4) was used to evaluate the effects of concentration of spores (2·0 × 10⁶, 4·0 × 10⁶ and 8·0 × 10⁶ spores ml^?1) and incubation time (3·0, 3·5, 4·0 and 4·5 h) on the response of the bioassay. Then, desirability function to raise the detection capabilities (CC_β) of tetracyclines and increase sensitivity to betalactams was implemented. Significant effects of Log[S] and incubation time [It] on the CC_β of betalactams and tetracyclines were observed. Finally, high value of global desirability (D = 0·853), adequate betalactams CC_β (3·8 μg l^?1 of penicillin ‘G’, 27 μg l^?1 of oxacillin, 8·1 μg l^?1 of ampicillin, 48 μg l^?1 of cloxacillin) and high tetracyclines CC_β (5260 μg l^?1 chlortetracycline, 1550 μg l^?1 of oxytetracycline, 1070 μg l^?1 of tetracycline) were calculated. Conclusions: The application of chemometric tools allows the optimization of a bioassay that detects betalactam residues in milk. The more robust conditions have been achieved in Log[S] = 6·30 and [It] = 4·20 h. Significance and Impact of the Study: The logistic regression model and the desirability function are adequate chemometric techniques to improve the properties of the methods, because it is possible to increase sensitivity and decrease cross‐specificity simultaneously.     

The uncertain role of diversity dependence in species diversification and the need to incorporate time-varying carrying capacities

There is no agreement among palaeobiologists or biologists as to whether, or to what extent, there are limits on diversification and species numbers. Here, we posit that part of the disagreement stems from: (i) the lack of explicit criteria for defining the relevant species pools, which may be defined phylogenetically, ecologically or geographically; (ii) assumptions that must be made when extrapolating from population-level logistic growth to macro-evolutionary diversification; and (iii) too much emphasis being placed on fixed carrying capacities, rather than taking into account the opportunities for increased species richness on evolutionary timescales, for example, owing to increased biologically available energy, increased habitat complexity and the ability of many clades to better extract resources from the environment, or to broaden their resource base. Thus, we argue that a more effective way of assessing the evidence for and against the ideas of bound versus unbound diversification is through appropriate definition of the relevant species pools, and through explicit modelling of diversity-dependent diversification with time-varying carrying capacities. Here, we show that time-varying carrying capacities, either increases or decreases, can be accommodated through changing intrinsic diversification rates (diversity-independent effects), or changing the effects of crowding (diversity-dependent effects).     

Evaluating models to assess the distribution of Buxus balearica in southern Spain

Question: Which is the best model to predict the habitat distribution of Buxus balearica Lam. in southern Spain? Location: Málaga and Granada, Spain, across an area of 38 180 km². Methods: Prediction models based on 17 environmental variables were tested. Six methods were compared: multivariate adaptive regression spline (MARS), maximum entropy approach to modelling species' distributions (Maxent), two generic algorithms based on environmental metrics dissimilarity (BIOCLIM and DOMAIN), Genetic Algorithm for Rule‐set Prediction (GARP), and supervised learning methods based on generalized linear classifiers (support vector machines, SVMs). To test the predictive power of the models we used the Kappa index. Results: Maxent most accurately predicted the habitat distribution of B. balearica, followed by MARS models. The other models tested yielded lower accuracy values. A comparison of the predictive power of the models revealed that climate variables made the highest contributions among the environmental variables studied. The variables that made the lowest contributions were the insolation models. To examine the sensitivity of the models to a reduction in the number of variables, a test showed that accuracy of over 0.90 was maintained by applying just three climatic variables (spring rainfall, mean temperature of the warmest month, and mean temperature of the coldest month). Maps derived from the algorithms of all models tested coincided well with the known distribution of the species. Conclusions: Model habitat prediction is a preliminary step towards highlighting areas of high habitat suitability of B. balearica. These data support the results of previous research, which show that MaxEnt is the best technique for modelling species distributions with small sample sizes.     

Factors associated with the occurrence of Triatoma sordida (Hemiptera: Reduviidae) in rural localities of Central-West Brazil

This study estimates the factors of artificial environments (houses and peridomestic areas) associated with Triatoma sordida occurrence. Manual searches for triatomines were performed in 136 domiciliary units (DUs) in two rural localities of Central-West Brazil. For each DU, 32 structural, 23 biotic and 28 management variables were obtained. Multiple logistic regression analysis was performed in order to identify statistically significant variables associated with occurrence of T. sordida in the study areas. A total of 1,057 specimens (99% in peridomiciles, mainly chicken coops) of T. sordida were collected from 63 DUs (infestation: 47%; density: ~8 specimens/DU; crowding: ~17 specimens/infested DU; colonisation: 81%). Only six (0.6%) out of 945 specimens examined were infected with Trypanosoma cruzi. The final adjusted logistic regression model indicated that the probability of T. sordida occurrence was higher in DU with wooden chicken coops, presence of > 30 animals in wooden corrals, presence of wood piles and presence of food storeroom. The results show the persistence of T. sordida in peridomestic habitats in rural localities of Central-West Brazil. However, the observed low intradomestic colonisation and minimal triatomine infection rates indicate that T. sordida has low potential to sustain high rates of T. cruzi transmission to residents of these localities.     

Searching for a Needle in a Haystack: Evaluating Survey Methods for Non-indigenous Plant Species

The control and management of non-indigenous plant species (NIS) can be conceptually divided into three phases: inventory/survey, monitoring and management. Here we focus on phase one, determining which species are present and where they are located within the environment. Sampling for NIS is inherently time-consuming and thus costly. Many management areas are large and therefore can only be surveyed (partial observation of the total area by sampling) and not inventoried (total observation of area). Survey data should reflect the spatial distribution of the target species populations over the landscape. Such data can then be used in combination with environmental data, to create probability maps of target species occurrence for the entire area of interest. We used a GIS model to evaluate seven different survey methods for consistency and reliability of intersecting NIS species’ patches and producing samples which reflect the spatial distribution of the population, and which can be performed in a cost and time-efficient manner. The GIS model was developed to create NIS populations which were then sampled using the different survey methods, and the results recorded. To improve the applicability of the model, four patch sizes and levels of occurrence were used, along with random and weighted distribution patterns in relation to patch proximity to roads and trails. Grid and random points, and targeted (stratified continuous) transects (starting on a road or trail (rights of way (RoW)) and finishing 2 km from any RoW) methods provided the most consistent samples of the population. Logistically, point methods required an unrealistic distance and time commitment in comparison with transect methods. The importance of collecting information on the size of NIS patches was demonstrated as more small patches were intersected than larger ones when the area infested was held constant. Thus, if frequency of patches is used to explain the results of a survey then comparisons between species and methods are difficult to interpret thus leading to erroneous conclusions. However, use of percentage of area infested estimates provides for easier comparison between species and sample methods. The targeted transect method provided the most reliable, efficient and consistent sample with the expected spatial distribution.     

A Bootstrap-Based Covariate Selection Method for Modeling the Risk of Lightning-Induced Fires at a Local Scale: A Case Study in Northwest Spain

In lightning-induced fire risk prediction models, the number of potential predictors is usually high, with some redundancy among them. It is therefore important to select the best subset of predictors that obtain models with the greatest discrimination capacity. With this aim in mind, the logistic generalized linear model was used to estimate lightning-induced fire occurrence using a case study of the province of León (northwest Spain). A bootstrap-based test was used to obtain the optimal number of predictors and to model this optimal number of predictors displaying the largest area under the receiver operating characteristics curve. The results show that of the 16 variables initially considered, only three were necessary to obtain the model with the best discriminatory capacity for estimating lightning-induced fire occurrence. Moreover, this model can be considered equivalent to another nine alternative models with three covariates. Both the optimal and the equivalent models are useful in the spatially explicit assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and the design of long-term wildfire management strategies. The methodology used for this case study can be applied to other wildfire risk assessment situations where multiple and interconnected covariates are available.     

An integrative approach to species discovery in odonates: from character‐based DNA barcoding to ecology

Modern taxonomy requires an analytical approach incorporating all lines of evidence into decision‐making. Such an approach can enhance both species identification and species discovery. The character‐based DNA barcode method provides a molecular data set that can be incorporated into classical taxonomic data such that the discovery of new species can be made in an analytical framework that includes multiple sources of data. We here illustrate such a corroborative framework in a dragonfly model system that permits the discovery of two new, but visually cryptic species. In the African dragonfly genus Trithemis three distinct genetic clusters can be detected which could not be identified by using classical taxonomic characters. In order to test the hypothesis of two new species, DNA‐barcodes from different sequence markers (ND1 and COI) were combined with morphological, ecological and biogeographic data sets. Phylogenetic analyses and incorporation of all data sets into a scheme called taxonomic circle highly supports the hypothesis of two new species. Our case study suggests an analytical approach to modern taxonomy that integrates data sets from different disciplines, thereby increasing the ease and reliability of both species discovery and species assignment.     

Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery

Emerging plant pathogens are a significant problem for conservation and food security. Surveillance is often instigated in an attempt to detect an invading epidemic before it gets out of control. Yet in practice many epidemics are not discovered until already at a high prevalence, partly due to a lack of quantitative understanding of how surveillance effort and the dynamics of an invading epidemic relate. We test a simple rule of thumb to determine, for a surveillance programme taking a fixed number of samples at regular intervals, the distribution of the prevalence an epidemic will have reached on first discovery (discovery-prevalence) and its expectation E(q*). We show that E(q*) = r/(N/Δ), i.e. simply the rate of epidemic growth divided by the rate of sampling; where r is the epidemic growth rate, N is the sample size and Δ is the time between sampling rounds. We demonstrate the robustness of this rule of thumb using spatio-temporal epidemic models as well as data from real epidemics. Our work supports the view that, for the purposes of early detection surveillance, simple models can provide useful insights in apparently complex systems. The insight can inform decisions on surveillance resource allocation in plant health and has potential applicability to invasive species generally.     

Offspring performance in dynamic habitats: key factors for a riparian carabid beetle

Abstract.  1. Knowledge of the ecology of carabid species is largely restricted to adults, although larval mortality is assumed to be the key factor in overall mortality. As low-mobility larvae are unable to avoid unfavourable conditions, habitat selection of reproducing adults should be clearly affected by the habitat factors which determine offspring performance.
2. The present study examines the key habitat factors governing the distribution patterns of Bembidion velox larvae and adults on the river banks of the River Elbe by means of habitat suitability models. The validity of the determined habitat factors for offspring performance and survival was tested in laboratory experiments.
3. In the field, B. velox adults as well as larvae show a strong association with semi-terrestrial, sandy, open soil habitats.
4. In the laboratory, overall mortality of larvae reared in different substrates was lowest in sand of medium grain size mixed with fine and coarse sand. The first larval instars in particular reacted sensitively to variations in grain size. Furthermore, flood resistance of eggs was demonstrated, as 90% of larvae hatched under permanently flooded conditions.
5. Short development times were recorded, with 4–7 days for hatching of young larvae from eggs after oviposition and approximately 28 days for the development of adults from newly hatched larvae. This probably increases the survival probability for the pre-imaginal stages in dynamic habitats.
6. In conclusion, it can be said that reproductive success is strongly dependent on oviposition site selection by adults as this reflects the ecological demands of the immature stages.