Delaying conservation actions for improved knowledge: how long should we wait?

Decisions about where conservation actions are implemented are based on incomplete knowledge about biodiversity. The Protea Atlas is a comprehensive database, containing information collated over a decade. Using this data set in a series of retrospective simulations, we compared the outcome from different scenarios of information gain, and habitat protection and loss, over a 20-year period. We assumed that there was no information on proteas at the beginning of the simulation but knowledge improved each year. Our aim was to find out how much time we should spend collecting data before protecting habitat when there is ongoing loss of habitat. We found that, in this case, surveying for more than 2 years rarely increased the effectiveness of conservation decisions in terms of representation of proteas in protected areas and retention within the landscape. If the delay is too long, it can sometimes be more effective just using a readily available habitat map. These results reveal the opportunity costs of delaying conservation action to improve knowledge.     

Examining and Fine-tuning the Selection of Glycan Compositions with GlyConnect Compozitor

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Highlights

• •New comparative tool to assess the relevance of a glycan composition file to be used in a glycoproteomic study.
• •User-friendly and web-based interface to explore glycoproteomics data.
• •Detection of glycan compositional trends within and across protein(s) or tissues.

     

Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions: A Bayesian approach

Joint modeling of longitudinal data and survival data has been used widely for analyzing AIDS clinical trials, where a biological marker such as CD4 count measurement can be an important predictor of survival. In most of these studies, a normal distribution is used for modeling longitudinal responses, which leads to vulnerable inference in the presence of outliers in longitudinal measurements. Powerful distributions for robust analysis are normal/independent distributions, which include univariate and multivariate versions of the Student's t, the slash and the contaminated normal distributions in addition to the normal. In this paper, a linear‐mixed effects model with normal/independent distribution for both random effects and residuals and Cox's model for survival time are used. For estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. Some simulation studies are performed for illustration of the proposed method. Also, the method is illustrated on a real AIDS data set and the best model is selected using some criteria.     

Integrating the Rabinowitz rarity framework with a National Plant Inventory in South Korea

Increasingly large presence‐only survey datasets are becoming available for use in conservation assessments. Potentially, these records could be used to determine spatial patterns of plant species rarity and endemism. We test the integration of a large South Korean species record database with Rabinowitz rarity classes. Rabinowitz proposed seven classes of species rarity using three variables: geographic range, habitat specificity, and local population size. We estimated the range size and local abundance of 2,215 plant species from species occurrence records and habitat specificity as the number of landcover types each species’ records were found in. We classified each species into a rarity class or as common, compared species composition by class to national lists, and mapped the spatial pattern of species richness for each rarity class. Species were classed to narrow or wide geographic ranges using 315 km, the average from a range size index of all species (D_max), based on maximum distance between observations. There were four classes each within the narrow and wide range groups, sorted using cutoffs of local abundance and habitat specificity. Nationally listed endangered species only appeared in the narrow‐range classes, while nationally listed endemic species appeared in almost all classes. Species richness in most rarity classes was high in northeastern South Korea especially for species with narrow ranges. Policy implications. Large presence‐only surveys may be able to estimate some classes of rarity better than others, but modification to include estimates of local abundance and habitat types, could greatly increase their utility. Application of the Rabinowitz rarity framework to such surveys can extend their utility beyond species distribution models and can identify areas that need further surveys and for conservation priority. Future studies should be aware of the subjectivity of the rarity classification and that regional scale implementations of the framework may differ.     

From Scilla to Charybdis – is our voyage safer now?

The genus Charybdis Speta (previously Urginea maritima agg.) was investigated karyologically and genetically throughout its geographic range in the Mediterranean. The different ploidy levels show a strong geographic pattern. Diploid populations are mainly found along the northern coast of Africa with C. pancration extending northwards from Tunisia to southern Italy. Tetraploid populations are most densely distributed in the eastern Mediterranean but are also found in North Africa, on the Balearic and Canary Islands. Hexaploid populations are restricted to the Iberian Peninsula and adjacent Morocco and Algeria. Chloroplast microsatellite data suggest that determination of ploidy levels alone is insufficient to adequately describe the existing populations. Especially the tetraploid and hexaploid populations exhibit additional genetic differentiation and geographic structuring. AFLP data indicate that tetraploid populations from southern Italy are of hybrid origin. Phylogenetic analysis further revealed that the genera Urginea Steinh. and Charybdis are not directly related to each other but have strong ties to genera from southern Africa. A possible colonization scenario of the Mediterranean via NW Africa and Iberia is discussed.     

A Multiple‐Absorbent Markov Process in Survival Studies: Application to Breast Cancer

A Markov process with several absorbent states is applied for analyzing a breast cancer dataset. The study examines the evolution of patients until death, and shows that two well‐differentiated ways can be considered in the evolution of patients towards the death state: those who relapse and those who not. The risk groups we have considered are determined by the application of treatments radiotherapy and chemotherapy, which are introduced as covariates. Four states are distinguished: no relapse, relapse, death after metastasis, and death without metastasis, the last two absorbent. We apply a methodology that uses algorithmic procedures, avoiding differential equations. The transition probability functions and the likelihood function in the model are calculated. For the dataset, the survival functions and the mean times in states for the different group of risks are determined. We show that the metastasis is the main cause of death in this cohort, but the number of deaths by relapse is not negligible.     

Factors affecting the fruiting of bilberries: an analysis of categorical data set

The present study is an application of categorical data analysis in ecological research. The approach is based on logistic regression following an exploratory graphical analysis. The material was collected in an extensive forest inventory in which a set of observations was made by eye in a stratified random sample of 262 mature upland forest stands in South Finland. The problem was to interpret the variation in the fertility of the tillers of bilberry (Vaccinium myrtillus). In the field, the fertility was recorded as a three-class ordinal variable. The information available for the interpretation included the visually estimated density of the tree crowns, the soil fertility class determined using Cajander's forest site types and the percent cover of V. myrtillus.The GLM framework was employed in successive stages of the data analysis in order to find a model to fit the data. For this, the three-class ordinal response variable was reduced to two classes: stands characterized by (a) sterile and (b) fertile bilberry tillers. Successful prediction of the distribution of these two types of forest stands was achieved with a logistic-regression model by using canopy coverage and soil fertility classes as predictor variables. The generalized linear modelling framework is suitable for studying many ecological problems even when only rough categorical estimates of environmental scalars are available.