Hormone and Metabolite Profiles in Nesting Green and Flatback Turtles: Turtle Species with Different Life Histories

Herbivorous turtle, Chelonia mydas, inhabiting the south China Sea and breeding in Peninsular Malaysia, and Natator depressus, a carnivorous turtle inhabiting the Great Barrier Reef and breeding at Curtis Island in Queensland, Australia, differ both in diet and life history. Analysis of plasma metabolites levels and six sex steroid hormones during the peak of their nesting season in both species showed hormonal and metabolite variations. When compared with results from other studies progesterone levels were the highest whereas dihydrotestosterone was the plasma steroid hormone present at the lowest concentration in both C. mydas and N. depressus plasma. Interestingly, oestrone was observed at relatively high concentrations in comparison to oestradiol levels recorded in previous studies suggesting that it plays a significant role in nesting turtles. Also, hormonal correlations between the studied species indicate unique physiological interactions during nesting. Pearson correlation analysis showed that in N. depressus the time of oviposition was associated with elevations in both plasma corticosterone and oestrone levels. Therefore, we conclude that corticosterone and oestrone may influence nesting behaviour and physiology in N. depressus. To summarise, these two nesting turtle species can be distinguished based on the hormonal profile of oestrone, progesterone, and testosterone using discriminant analysis.

Morphometric analysis of telencephalic structure in a variety of neognath and paleognath bird species reveals regional differences associated with specific behavioral traits

Birds exhibit a huge array of behavior, ecology and physiology, and occupy nearly every environment on earth, ranging from the desert outback of Australia to the tropical rain forests of Panama. Some birds have adopted a fully nocturnal lifestyle, such as the barn owl and kiwi, while others, such as the albatross, spend nearly their entire life flying over the ocean. Each species has evolved unique adaptations over millions of years to function in their respective niche. In order to increase processing power or network efficiency, many of these adaptations require enlargements and/or specializations of the brain as a whole or of specific brain regions. In this study, we examine the relative size and morphology of 9 telencephalic regions in a number of Paleognath and Neognath birds and relate the findings to differences in behavior and sensory ecology. We pay particular attention to those species that have undergone a relative enlargement of the telencephalon to determine whether this relative increase in telencephalic size is homogeneous across different brain regions or whether particular regions have become differentially enlarged. The analysis indicates that changes in the relative size of telencephalic regions are not homogeneous, with every species showing hypertrophy or hypotrophy of at least one of them. The three-dimensional structure of these regions in different species was also variable, in particular that of the mesopallium in kiwi. The findings from this study provide further evidence that the changes in relative brain size in birds reflect a process of mosaic evolution.

Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks

Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

MRI of postmortem specimens of endangered species for comparative brain anatomy

Many of the 5,500 threatened species of vertebrates found worldwide are highly protected and generally unavailable for scientific investigation. Here we describe a noninvasive protocol to visualize the structure and size of brain in postmortem specimens. We demonstrate its utility by examining four endangered species of kiwi (Apteryx spp.). Frozen specimens are thawed and imaged using MRI, revealing internal details of brain structure. External brain morphology and an estimate of brain volume can be reliably obtained by creating 3D models. This method has facilitated a comparison of brain structure in the different kiwi species, one of which is on the brink of extinction. This new approach has the potential to extend our knowledge of brain structure to species that have until now been outside the reach of anatomical investigation.

Morphology, echolocation and foraging behaviour in two sympatric sibling species of bat (Tylonycteris pachypus and Tylonycteris robustula) (Chiroptera : Vespertilionidae)

We studied the wing morphology, echolocation calls, foraging behaviour and flight speed of Tylonycteris pachypus and Tylonycteris robustula in Longzhou County, South China during the summer (June–August) of 2005. The wingspan, wing loading and aspect ratio of the two species were relatively low, and those of T. pachypus were lower compared with T. robustula. The echolocation calls of T. pachypus and T. robustula consist of a broadband frequency modulated (FM) sweep followed by a short narrowband FM sweep. The dominant frequency of calls of T. pachypus was 65.1 kHz, whereas that of T. robustula was 57.7 kHz. The call frequencies (including highest frequency of the call, lowest frequency of the call and frequency of the call that contained most energy) of T. pachypus were higher than those of T. robustula, and the pulse duration of the former was longer than that of the latter. The inter-pulse interval and bandwidth of the calls were not significantly different between the two species. Tylonycteris pachypus foraged in more complex environments than T. robustula, although the two species were both netted in edge habitats (around trees or houses), along pathways and in the tops of trees. Tylonycteris pachypus flew slower (straight level flight speed, 4.3 m s−1) than T. robustula (straight level flight speed, 4.8 m s−1). We discuss the relationship between wing morphology, echolocation calls, foraging behaviour and flight speed, and demonstrate resource partitioning between these two species in terms of morphological and behavioural factors.

Genetic divergence and echolocation call frequency in cryptic species of Hipposideros larvatus s.l. (Chiroptera : Hipposideridae) from the Indo-Malayan region

The intermediate leaf-nosed bat (Hipposideros larvatus) is a medium-sized bat distributed throughout the Indo-Malay region. In north-east India, bats identified as H. larvatus captured at a single cave emitted echolocation calls with a bimodal distribution of peak frequencies, around either 85 kHz or 98 kHz. Individuals echolocating at 85 kHz had larger ears and longer forearms than those echolocating at 98 kHz, although no differences were detected in either wing morphology or diet, suggesting limited resource partitioning. A comparison of mitochondrial control region haplotypes of the two phonic types with individuals sampled from across the Indo-Malay range supports the hypothesis that, in India, two cryptic species are present. The Indian 98-kHz phonic bats formed a monophyletic clade with bats from all other regional populations sampled, to the exclusion of the Indian 85-kHz bats. In India, the two forms showed 12–13% sequence divergence and we propose that the name Hipposideros khasiana for bats of the 85-kHz phonic type. Bats of the 98-kHz phonic type formed a monophyletic group with bats from Myanmar, and corresponded to Hipposideros grandis, which is suggested to be a species distinct from Hipposideros larvatus. Differences in echolocation call frequency among populations did not reflect phylogenetic relationships, indicating that call frequency is a poor indicator of evolutionary history. Instead, divergence in call frequency probably occurs in allopatry, possibly augmented by character displacement on secondary contact to facilitate intraspecific communication.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Capturing natural-colour 3D models of insects for species discovery and diagnostics

Collections of biological specimens are fundamental to scientific understanding and characterization of natural diversity - past, present and future. This paper presents a system for liberating useful information from physical collections by bringing specimens into the digital domain so they can be more readily shared, analyzed, annotated and compared. It focuses on insects and is strongly motivated by the desire to accelerate and augment current practices in insect taxonomy which predominantly use text, 2D diagrams and images to describe and characterize species. While these traditional kinds of descriptions are informative and useful, they cannot cover insect specimens "from all angles" and precious specimens are still exchanged between researchers and collections for this reason. Furthermore, insects can be complex in structure and pose many challenges to computer vision systems. We present a new prototype for a practical, cost-effective system of off-the-shelf components to acquire natural-colour 3D models of insects from around 3 mm to 30 mm in length. ("Natural-colour" is used to contrast with "false-colour", i.e., colour generated from, or applied to, gray-scale data post-acquisition.) Colour images are captured from different angles and focal depths using a digital single lens reflex (DSLR) camera rig and two-axis turntable. These 2D images are processed into 3D reconstructions using software based on a visual hull algorithm. The resulting models are compact (around 10 megabytes), afford excellent optical resolution, and can be readily embedded into documents and web pages, as well as viewed on mobile devices. The system is portable, safe, relatively affordable, and complements the sort of volumetric data that can be acquired by computed tomography. This system provides a new way to augment the description and documentation of insect species holotypes, reducing the need to handle or ship specimens. It opens up new opportunities to collect data for research, education, art, entertainment, biodiversity assessment and biosecurity control. © 2014 Nguyen et al.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Productivity is a poor predictor of plant species richness

For more than 30 years, the relationship between net primary productivity and species richness has generated intense debate in ecology about the processes regulating local diversity. The original view, which is still widely accepted, holds that the relationship is hump-shaped, with richness first rising and then declining with increasing productivity. Although recent meta-analyses questioned the generality of hump-shaped patterns, these syntheses have been criticized for failing to account for methodological differences among studies. We addressed such concerns by conducting standardized sampling in 48 herbaceous-dominated plant communities on five continents. We found no clear relationship between productivity and fine-scale (meters−2) richness within sites, within regions, or across the globe. Ecologists should focus on fresh, mechanistic approaches to understanding the multivariate links between productivity and richness.

Taxonomic review of the chironomid genus Cricotopus v.d. Wulp (Diptera: Chironomidae) from Australia : keys to males, females, pupae and larvae, description of ten new species and comments on Paratrichocladius Santos Abreu

The Australian species of the Orthocladiinae genus Cricotopus Wulp (Diptera: Chironomidae) are revised for larval, pupal, adult male and female life stages. Eleven species, ten of which are new, are recognised and keyed, namely Cricotopus acornis Drayson & Cranston sp. nov., Cricotopus albitarsis Hergstrom sp. nov., Cricotopus annuliventris (Skuse), Cricotopus brevicornis Drayson & Cranston sp. nov., Cricotopus conicornis Drayson & Cranston sp. nov., Cricotopus hillmani Drayson & Cranston, sp. nov., Cricotopus howensis Cranston sp. nov., Cricotopus parbicinctus Hergstrom sp. nov., Cricotopus tasmania Drayson & Cranston sp. nov., Cricotopus varicornis Drayson & Cranston sp. nov. and Cricotopus wangi Cranston & Krosch sp. nov. Using data from this study, we consider the wider utility of morphological and molecular diagnostic tools in untangling species diversity in the Chironomidae. Morphological support for distinguishing Cricotopus from Paratrichocladius Santo-Abreu in larval and pupal stages appears lacking for Australian taxa and brief notes are provided concerning this matter.

Optimal allocation of conservation effort among subpopulations of a threatened species: How important is patch quality?

Money is often a limiting factor in conservation, and attempting to conserve endangered species can be costly. Consequently, a framework for optimizing fiscally constrained conservation decisions for a single species is needed. In this paper we find the optimal budget allocation among isolated subpopulations of a threatened species to minimize local extinction probability. We solve the problem using stochastic dynamic programming, derive a useful and simple alternative guideline for allocating funds, and test its performance using forward simulation. The model considers subpopulations that persist in habitat patches of differing quality, which in our model is reflected in different relationships between money invested and extinction risk. We discover that, in most cases, subpopulations that are less efficient to manage should receive more money than those that are more efficient to manage, due to higher investment needed to reduce extinction risk. Our simple investment guideline performs almost as well as the exact optimal strategy. We illustrate our approach with a case study of the management of the Sumatran tiger, Panthera tigris sumatrae, in Kerinci Seblat National Park (KSNP), Indonesia. We find that different budgets should be allocated to the separate tiger subpopulations in KSNP. The subpopulation that is not at risk of extinction does not require any management investment. Based on the combination of risks of extinction and habitat quality, the optimal allocation for these particular tiger subpopulations is an unusual case: subpopulations that occur in higher-quality habitat (more efficient to manage) should receive more funds than the remaining subpopulation that is in lower-quality habitat. Because the yearly budget allocated to the KSNP for tiger conservation is small, to guarantee the persistence of all the subpopulations that are currently under threat we need to prioritize those that are easier to save. When allocating resources among subpopulations of a threatened species, the combined effects of differences in habitat quality, cost of action, and current subpopulation probability of extinction need to be integrated. We provide a useful guideline for allocating resources among isolated subpopulations of any threatened species. © 2010 by the Ecological Society of America.