Exploring the potential of two-aged white spruce plantations for the production of sawlog volume with simulations using SORTIE-ND

The main objective for even-aged plantation (EAP) management of producing sawlog material has driven practices towards low initial planting densities and lower post thinning densities. For semi-shade tolerant species, the resulting stand density potentially leaves enough growing space for the introduction of a second cohort of trees in the understory, making it a two-aged plantation (TAP). TAPs could have many silvicultural benefits, especially in sensitive areas where intensive treatments associated with EAPs are incompatible with local management objectives. White spruce (Picea glauca) is a good candidate species for modeling TAPs because it is the most widely planted tree species in Canada and has proven tolerance to understory planting. SORTIE-ND, a single-tree spatially explicit growth model was used to explore the yield of variable density and rotation length scenarios when each white spruce cohort is introduced mid rotation, compared to traditional even-aged management. All TAP scenarios tested produced more sawlog volume and more merchantable volume than equivalent densities of EAPs. The lowest density tested, 400 stems ha-1 planted every 35 years, had the highest sawlog yields (3.23 m3 ha-1 yr-1). Considering smaller size products changes the optimum TAP scenario but maintains the advantage over EAPs

A polymerase chain reaction-based cloning strategy applicable to functional microRNA studies

MicroRNAs (miRNAs) are key regulatory RNAs that act in concert to coordinately control messenger RNA translation through imperfect recognition of multiple specific binding sites (BSs) located in their 30 untranslated region. Here, we present a polymerase chain reaction-based cloning strategy that allows the rapid and efficient generation of regulatory elements harboring up to 10 miRNA BSs. Amenable for the study of regulatory elements of any multiplicity, such as those recognized by miRNAs and transcription factors, this methodology will facilitate functional miRNA/miRNA BS studies and accelerate discoveries mainly in the field of gene regulation

Improving the workflow to crack Small, Unbalanced, Noisy, but Genuine (SUNG) datasets in bioacoustics: The case of bonobo calls.

Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy

Controlling for call types in the individual signature task.

Controlling for call types in the individual signature task.</p

Examples of spectrograms of the five call types.

Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy.</div

Average confusion matrix, for 100 iterations of the evaluation process, reporting the classification rates of the individual signatures in the best configuration (the ensemble classifier combining the 9 primary classifiers).

Individuals are sorted from bottom to top by decreasing the number of calls (Jill: largest number; Busira: lowest number). Percentages are according to the reference and sum to 1 along rows. The value of a cell color is proportional to its percentage (the darker, the larger).</p

Number of calls per individual and per call type in the dataset used for automatic classification.

The five call types are: Bark (B), Peep (P), Peep Yelp (PY), Soft Bark (SB), and Scream Bark (SCB).</p

Average importance of acoustic features, for 100 iterations of the evaluation process, when classifying individual signatures with xgboost.

Left. Features of the Bioacoustic set. Right. Features of the DCT set. The bar plots illustrate the relative influence of each acoustic feature on the classification performance. The error bars report the standard deviation of the measure of importance for the 100 iterations of the evaluation process.</p