RESUMO
Premise: Aquatic macrophyte species abundance and nutrient affinity are used in metrics to assess the trophic condition of lakes and rivers. The development of these indices is often regional, with inter-regional comparisons being complicated by the lack of taxonomic overlap. Here, we use a traits-based approach to expand the geographic scope of existing metrics. Methods: We generalized European trophic affinity values using the response of plant growth form to the light-nutrient gradient, then applied these values to sites in Canada. We evaluated the method's performance against the measured total phosphorus concentration (TP). Results: Free-floating and emergent growth forms were associated with enriched waters (>0.2 mg/L TP), whereas rosette forms were associated with oligotrophic conditions (<0.05 mg/L TP). The responses were longitudinally consistent, and the site scores among indices were highly collinear. Growth form-based scores were more strongly correlated with TP than were species-based scores (0.42-0.56 versus 0.008-0.25). Discussion: We leveraged the ecological relationship between increased surface water nutrient enrichment and the dominance of particular aquatic plant growth forms to generalize aquatic plant trophic indices. We demonstrated an approach for adapting species-based indices to plant traits to facilitate a broader geographic application and simpler data collection, which could be used to develop an easily applied trait-based method of assessing water nutrient status.
RESUMO
PREMISE: Species identification is vital to many disciplines. Digital technology has improved identification tools, but the direct use of characters with continuous states has yet to be fully realized. To achieve full use of continuous characters for identification, I propose a classifier that calculates a posterior probability (degree of belief) in possible name assignments and an estimate of the relative evidence for the candidate annotations. METHODS: A model for a species is defined using continuous morphological characters, and an algorithm for identification with a naive Bayesian classifier, using the model, is presented. A method of estimating the strength of evidence for candidate species is also described. RESULTS: The proposed method is applied in two example identifications: native vs. invasive Myriophyllum in North America and vegetative Rhipidocladum bamboos in Mexico. In each instance, the new method provides a probability and estimate of the strength of the probability to enhance the name assignment in situations where taxa are difficult to differentiate using discrete character states. DISCUSSION: Naive Bayesian classifiers take advantage of the predictive information inherent in continuous morphological characters. Application of this methodology to plant taxonomy advances our ability to leverage digital technology for improved interactive taxonomic identifications.
RESUMO
We present the first multi-locus chloroplast phylogeny of Arthrostylidiinae, a subtribe of neotropical woody bamboos. The morphological diversity of Arthrostylidiinae makes its taxonomy difficult and prior molecular analyses of bamboos have lacked breadth of sampling within the subtribe, leaving internal relationships uncertain. We sampled 51 taxa, chosen to span the range of taxonomic diversity and morphology, and analyzed a combined chloroplast DNA dataset with six chloroplast regions: ndhF, trnD-trnT, trnC-rpoB, rps16-trnQ, trnT-trnL, and rpl16. A consensus of maximum parsimony and Bayesian inference analyses reveals monophyly of the Arthrostylidiinae and four moderately supported lineages within it. Six previously recognized genera were monophyletic, three polyphyletic, and two monotypic; Rhipidocladum sect. Didymogonyx is here raised to generic status. When mapped onto our topology, many of the morphological characters show homoplasy.
