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We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are ‘noisy’, with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both ‘hotspots’, which are frequently sampled locations, and ‘coldspots’, which are areas far from recent sampling, suggesting that an optimal sampling regime balances ‘hotspot’ sampling with a spread across the landscape.

Passive acoustic monitoring (PAM) is increasingly popular in ecological research and conservation programs, with high‐volume and long‐term data collection provided by automatized acoustic sensors offering unprecedented opportunities for faunal and ecosystem surveys. Practitioners and newcomers interested in PAM can easily find technical specifications for acoustic sensors and microphones, but guidelines on how to plan survey designs are largely scattered over the literature. Here, we (i) review spatial and temporal sampling designs used in passive acoustic monitoring, (ii) provide a synthesis of the crucial aspects of PAM survey design and (iii) propose a workflow to optimize recording autonomy and recording schedules. From 1992 to 2018, most of the 460 studies applying PAM in terrestrial environments have used a single recorder per site, covered broad spatial scales and rotated recorders between sites to optimize sampling effort. Continuous recording of specific diel periods was the main recording procedure used. When recording schedules were applied, a larger number of recordings per hour was generally associated with a smaller recording length. For PAM survey design, we proposed to (i) estimate memory/battery autonomy and associated costs, (ii) assess signal detectability to optimize recording schedules in order to recover maximum biological information and (iii) evaluate cost‐benefit scenarios between sampling effort and budget to address potential biases from a given PAM survey design. Establishing standards for PAM data collection will improve the quality of inferences over the broad scope of PAM research and promote essential standardization for cross‐scale research to understand long‐term biodiversity trends in a changing world. We review and synthesise main aspects of spatial and temporal designs used in passive acoustic monitoring applied to ecological research. To promote integrative and cross‐scale research, we compile cautionary suggestions to plan survey designs while accounting for species detectability and workable budget. These procedures support optimising research in the broad scope of applications with acoustic monitoring.

We analysed the temporal variation of inbreeding, genetic variability and population structure in the Burlina (BUR) cattle breed. A total of 279 individuals were chosen for the analysis representing a period of 19 years (1991-2010) and analysed using 24 microsatellite markers. A total of 235 alleles were detected in the population with a mean of 9.79±3.91 alleles per locus. In the 19-year period, a stable pattern in the mean number of alleles was found. The mean observed heterozygosity was 0.63 and it was slightly lower than the expected in all birth year groups. Neither an increase nor a decrease in heterozygosity and inbreeding estimates were detected over the years, with the exception of the F IS index which was close to zero in two birth year groups: 2001-2002 and 2006. Absence of bottleneck events was proved and structure analysis revealed an increase in breed complexity over the years and a clear differentiation with the Italian Holstein Friesian cattle breed. Molecular markers were successfully applied in the monitoring of the genetic variability of BUR thus enabling the planning and the application of strategies for the in situ conservation of genetic resources and the improving of breed identity.

Abstract Ever since its emergence in 1984, the field of ancient DNA has struggled to overcome the challenges related to the decay of DNA molecules in the fossil record. With the recent development of high-throughput DNA sequencing technologies and molecular techniques tailored to ultra-damaged templates, it has now come of age, merging together approaches in phylogenomics, population genomics, epigenomics, and metagenomics. Leveraging on complete temporal sample series, ancient DNA provides direct access to the most important dimension in evolution—time, allowing a wealth of fundamental evolutionary processes to be addressed at unprecedented resolution. This review taps into the most recent findings in ancient DNA research to present analyses of ancient genomic and metagenomic data.

Aims Human‐induced pressures such as deforestation cause anthropogenic range contractions (ARCs). Such contractions present dynamic distributions that may engender data misrepresentations within species distribution models. The temporal bias of occurrence data—where occurrences represent distributions before (past bias) or after (recent bias) ARCs—underpins these data misrepresentations. Occurrence–habitat mismatching results when occurrences sampled before contractions are modelled with contemporary anthropogenic variables; niche truncation results when occurrences sampled after contractions are modelled without anthropogenic variables. Our understanding of their independent and interactive effects on model performance remains incomplete but is vital for developing good modelling protocols. Through a virtual ecologist approach, we demonstrate how these data misrepresentations manifest and investigate their effects on model performance. Location Virtual Southeast Asia. Methods Using 100 virtual species, we simulated ARCs with 100‐year land‐use data and generated temporally biased (past and recent) occurrence datasets. We modelled datasets with and without a contemporary land‐use variable (conventional modelling protocols) and with a temporally dynamic land‐use variable. We evaluated each model's ability to predict historical and contemporary distributions. Results Greater ARC resulted in greater occurrence–habitat mismatching for datasets with past bias and greater niche truncation for datasets with recent bias. Occurrence–habitat mismatching prevented models with the contemporary land‐use variable from predicting anthropogenic‐related absences, causing overpredictions of contemporary distributions. Although niche truncation caused underpredictions of historical distributions (environmentally suitable habitats), incorporating the contemporary land‐use variable resolved these underpredictions, even when mismatching occurred. Models with the temporally dynamic land‐use variable consistently outperformed models without. Main conclusions We showed how these data misrepresentations can degrade model performance, undermining their use for empirical research and conservation science. Given the ubiquity of ARCs, these data misrepresentations are likely inherent to most datasets. Therefore, we present a three‐step strategy for handling data misrepresentations: maximize the temporal range of anthropogenic predictors, exclude mismatched occurrences and test for residual data misrepresentations.

Although the field of deep learning has improved video-based dance classification, traditional models are computationally inefficient because they process redundant frames, and do not have the capability to center on discriminative key moments. In order to fill this gap, the present paper proposes the Reinforcement-based Attentive Temporal Sampling (RATS) framework. RATS proposes a new algorithmic framework in that it develops the classification as a sequential decision-making process. It is a three-part modular structure, which includes a custom feature extraction pipeline (based on 3D Convolutional Neural Networks (3DCNN)) to learn rich visual-motion representations, a Deep Q-Network (DQN) agent with Bidirectional Long Short-Term Memory (BiLSTM) memory to learn the optimal movement policy in the video, and a final classification head that predicts the dance style based on the state summary presented by the BiLSTM. This approach, while significantly reducing the computational complexity and focusing on important frames, significantly improves the accuracy of the model in recognizing complex dance styles; in such a way that in the evaluation on the Let’s Dance dataset, it achieved the accuracy of 92.1%, showing a 3.9% increase in accuracy and a 4% improvement in the F-measure, which indicates the outstanding efficiency and effectiveness of RATS.

Traffic analyses, particularly speed measurements, are highly valuable in terms of road safety and traffic management. In this paper, an analytical model is presented to measure the speed of a moving vehicle using an off-the-shelf video camera. The method utilizes the temporal sampling rate of the camera and several intrusion lines in order to estimate the probability density function (PDF) of a vehicle’s speed. The proposed model provides not only an accurate estimate of the speed, but also the possibility of being able to study the performance boundaries with respect to the camera frame rate as well as the placement and number of intrusion lines in advance. This analytical model is verified by comparing its PDF outputs with the results obtained via a simulation of the corresponding movements. In addition, as a proof-of-concept, the proposed model is implemented for a video-based vehicle speed measurement system. The experimental results demonstrate the model’s capability in terms of taking accurate measurements of the speed via a consideration of the temporal sampling rate and lowering the deviation by utilizing more intrusion lines. The analytical model is highly versatile and can be used as the core of various video-based speed measurement systems in transportation and surveillance applications.

The ‘rapid temporal processing’ and the ‘temporal sampling framework’ hypotheses have been proposed to account for the deficits in language and literacy development seen in specific language impairment and dyslexia. This paper reviews these hypotheses and concludes that the proposed causal chains between the presumed auditory processing deficits and the observed behavioural manifestation of the disorders are vague and not well established empirically. Several problems and limitations are identified. Most data concern correlations between distantly related tasks, and there is considerable heterogeneity and variability in performance as well as concerns about reliability and validity. Little attention is paid to the distinction between ostensibly perceptual and metalinguistic tasks or between implicit and explicit modes of performance, yet measures are assumed to be pure indicators of underlying processes or representations. The possibility that diagnostic categories do not refer to causally and behaviourally homogeneous groups needs to be taken seriously, taking into account genetic and neurodevelopmental studies to construct multiple-risk models. To make progress in the field, cognitive models of each task must be specified, including performance domains that are predicted to be deficient versus intact, testing multiple indicators of latent constructs and demonstrating construct reliability and validity.

Much progress has been made in research on the causal mechanisms of developmental dyslexia. In recent years, the “temporal sampling” account of dyslexia has evolved considerably, with contributions from neurogenetics and novel imaging methods resulting in a much more complex etiological view of the disorder. The original temporal sampling framework implicates disrupted neural entrainment to speech as a causal factor for atypical phonological representations. Yet, empirical findings have not provided clear evidence of a low-level etiology for this endophenotype. In contrast, the neural noise hypothesis presents a theoretical view of the manifestation of dyslexia from the level of genes to behavior. However, its relative novelty (published in 2017) means that empirical research focused on specific predictions is sparse. The current paper reviews dyslexia research using a dual framework from the temporal sampling and neural noise hypotheses and discusses the complementary nature of these two views of dyslexia. We present an argument for an integrated model of sensory temporal sampling as an etiological pathway for dyslexia. Finally, we conclude with a brief discussion of outstanding questions.

Metapopulations are dynamic, and population genetics can reveal both spatial and temporal metapopulation variation. Yet, population genetic studies often focus on samples collected within a single time period or combine samples taken across time periods due to limited resources and the assumption that these approaches capture patterns and processes occurring over decadal and longer temporal scales. However, this may leave important fine-scale temporal variation in genetic composition undetected, particularly for metapopulations in which dynamic populations are expected. We investigated temporal patterns of population genetic diversity, effective population size, and differentiation across three sample periods for a dryland amphibian metapopulation. We sampled nine distinct Arizona treefrog (Hyla (Dryophytes) wrightorum) breeding ponds in 2014, 2018/2019, and 2021 and genotyped 17 microsatellite loci to quantify spatial and temporal population genetic dynamics. Genetic diversity within and between populations varied significantly among years. Most notably, we identified a concerning decline in allelic richness across populations, with an average − 26.11% difference between a population’s first and last sample period. Effective population sizes were generally small (Ne < 100) and variable within and among populations over time, with many populations falling below common conservation thresholds by the final sample period. Trends in global genetic diversity, as measured by heterozygosity, and population differentiation were relatively consistent across all sampling periods. Overall, we found that “snapshot” or single-time sampling approaches may miss temporal variability in genetic composition that has important conservation implications, including early warning signs of decline in genetic diversity.