Detecting contact in language trees: a Bayesian phylogenetic model with horizontal transfer

Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS point cloud metrics. Field measurements of tree height and effective leaf area index (LAIe) were acquired from a total of 200 and 26 trees, respectively. Comparable accuracies were obtained in the tree height and LAI estimations using ALS and DAI data independently. Tree height estimated from DAI-based metrics (Percentile 90 (P90) and minimum height (MinH)) yielded a coefficient of determination (R2) = 0.71 and a root mean square error (RMSE) = 0.71 m while models derived from ALS-based metrics (P90) yielded an R2 = 0.80 and an RMSE = 0.55 m. The estimation of LAI from DAI-based NDVI using Percentile 99 (P99) yielded an R2 = 0.62 and an RMSE = 0.17 m2/m−2. A comparative analysis of LAI estimation using ALS-based metrics (laser penetration index (LPI), interquartile distance (IQ), and Percentile 30 (P30)) yielded an R2 = 0.75 and an RMSE = 0.14 m2/m−2. The results provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.

Articulating Babel: An approach to cultural evolution

In this chapter, I elaborate the population genetics perspective introduced in chapter 1 and adopted in the subsequent chapters. I submit more justification here for the position that there are indeed heuristic advantages in approaching language evolution on the model of population genetics, assuming that a language is a species but not an organism. However, I also argue that the linguistic species need not be a clone of any biological species, despite the fact that it shares several properties with the parasitic species. In fact, the proposed population genetics of language evolution is more than an analog of population genetics, although its heuristics has been very much inspired by the latter. A linguistic species must be defined on its own independent terms and its evolutionary properties hypothesized according to its own combination of ontological characteristics. These account for both the similarities and differences which it displays with its closest kin in biological evolution. The basic assumption is that there are general evolutionary principles which apply similarly to the linguistic and biological species. However, there are species-specific principles which distinguish them from each other, based in part on whether a species is of the Darwinian or Lamarckian kind, on whether traits are transmitted sexually or asexually, horizontally and/or vertically, on whether the default quality of copying in trait inheritance is with or without modification, etc. One cluster of factors that plays an important role in any theory of evolution is “ecology.”

Development of allometric models for above and belowground biomass in swidden cultivation fallows of Northern Laos

Tree height is a key indicator in forest ecology, reflecting tree growth status and ecosystem structure. Traditional methods of tree height measurement rely on ground-based measurements, which are limited by cost and time. In recent years, the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height. In this study, we utilized light detection and ranging and satellite data to extract spectral, vegetation, texture, polarization, terrain, and season features. By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. It was found that (a) the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation ( R 2 = 0.72, mean absolute error = 1.89 m), and the optimization process of Shapley additive explanations reduced 23 features, which improved the prediction accuracy ( R 2 = 0.80, mean absolute error = 1.58 m) and saved computational resources; (b) the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions; and (c) during 2018 to 2023, tree heights in Shenzhen were mainly concentrated in 6 to 14 m, and the spatial distribution has a strong autocorrelation. Tree canopy heights in winter are generally lower than those in summer, and the tree growth rate shows spatial heterogeneity. Overall, this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen. It offers valuable data support and a theoretical foundation for urban greening management and ecological protection.

Accurate estimation of tree position, diameter at breast height (DBH), and tree height measurements is an important task in forest inventory. Mobile Laser Scanning (MLS) is an important solution. However, the poor global navigation satellite system (GNSS) coverage under the canopy makes the MLS system unable to provide globally-consistent point cloud data, and thus, it cannot accurately estimate the forest attributes. SLAM could be an alternative for solutions dependent on GNSS. In this paper, a mobile phone with RGB-D SLAM was used to estimate tree position, DBH, and tree height in real-time. The main aims of this paper include (1) designing an algorithm to estimate the DBH and position of the tree using the point cloud from the time-of-flight (TOF) camera and camera pose; (2) designing an algorithm to measure tree height using the perspective projection principle of a camera and the camera pose; and (3) showing the measurement results to the observer using augmented reality (AR) technology to allow the observer to intuitively judge the accuracy of the measurement results and re-estimate the measurement results if needed. The device was tested in nine square plots with 12 m sides. The tree position estimations were unbiased and had a root mean square error (RMSE) of 0.12 m in both the x-axis and y-axis directions; the DBH estimations had a 0.33 cm (1.78%) BIAS and a 1.26 cm (6.39%) root mean square error (RMSE); the tree height estimations had a 0.15 m (1.08%) BIAS and a 1.11 m (7.43%) RMSE. The results showed that the mobile phone with RGB-D SLAM is a potential tool for obtaining accurate measurements of tree position, DBH, and tree height.

Complex vocal learning, the capacity to imitate new sounds, underpins the evolution of animal vocal cultures and song dialects and is a key prerequisite for human speech and song. Due to its relevance for the understanding of cultural evolution and the biology and evolution of language and music, the trait has gained much scholarly attention. However, while we have seen tremendous progress with respect to our understanding of its morphological, neurological and genetic aspects, its peculiar phylogenetic distribution has remained elusive. Intriguingly, animals as distinct as hummingbirds and humpback whales share well-developed vocal learning capacity in common with humans, while this ability is quite limited in nonhuman primates. Yet, solving this ‘vocal learning conundrum’ may shed light on the constraints ancestral humans overcame to unleash their vocal capacities. To this end I consider major constraints and functions that have been proposed. I highlight an especially promising ecological constraint, namely the spatial dimensionality of the environment. Based on an informal comparative review, I suggest that complex vocal learning is associated with three-dimensional habitats such as air and water. I argue that this is consistent with recent theoretical advances—i.e., the coercion-avoidance and dimensionality hypotheses—and with the long-standing hypothesis that mate choice is a major driver of the evolution and origin of complex vocal learning. However, I stress that multiple functions may apply and that quantitative phylogenetic comparative methods should be employed to finally resolve the issue.

Tree height is one of the key parameters in forest plantations that plays a crucial role in estimation of above-ground biomass (AGB) of trees and stands. The parameter may be obtained by different methods from airborne remotely sensed datasets such shadow length of each tree individual or crown height models (CHMs). However, tree height estimation based on shadow length might be biased considering diverse topography of forest sites. Therefore, this study aims to develop a reliable method to estimate tree height in a plantation forest using shadow length on UAV imagery. First, heights of 151 pine (Pinus eldarica) trees were precisely measured in Pardisan Park, North Khorasan province, Iran. Additionally, a collection of images was captured by a Phantom 4Pro UAV in order to illustrate the study area. Then, two different approaches were considered to estimate the height of trees. In the first approach, tree heights were estimated based on shadow length on the UAV orthomosaic and correcting the effect of slope. The second approach considered the UAV-based CHM and height estimation using CHM segmentation and local maximum filtering. The results showed that tree heights estimated by the first approach were not significantly different from the in-situ data (p=0.298). Furthermore, the heights estimated by the slope corrected shadow length showed higher precision compared to the heights estimated by the shadow length without slope correction (Relative root mean squared error, RRMSE 5.6% and 8.2% respectively). However, the heights obtained from the first approach were less precise than the second approach (RRMSE 5.6% and 4.2% respectively). In general, it was concluded that height estimation of pine trees based on shadow length after correction of slope effects can be considered as a reliable approach, although CHM is more efficient in estimating the tree heights. The findings of this study are applicable for height estimation of pine trees within plantation forests on UAV imagery.

We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

Comparative phylogenetic analyses based on linguistic data are useful for reconstructing the cultural evolution of recent expansions of humans around the world. It is an exciting time for phylogenetic comparative studies in lowland South America given the emergence of more comprehensive ethnolinguistic datasets. Phylogenetic methods can now be applied to more lowland language families to facilitate the study of pre-historical population expansions and cultural variation. This chapter investigates phylogenetic relationships among the six major lowland South American language families using structural linguistic data. Two cultural traits that are likely to have deep evolutionary histories that extend back to last common ancestors of several large language families include uxorilocal postmarital residence (women continue to live near natal families after marriage) and partible paternity beliefs (conception belief that multiple men can be co-genitors of one child). The Carib-Pano-Tupi-Jê clade is mostly uxorilocal with partible paternity beliefs in thirty-eight of forty-three societies.

Studies in cultural evolution have uncovered many types of social learning strategies that are adaptive in certain environments. The efficiency of these strategies also depends on the individual characteristics of both the observer and the demonstrator. We investigate the relationship between intelligence and the ways social and individual information is utilised to make decisions in an uncertain environment. We measure fluid intelligence and study experimentally how individuals learn from observing the choices of a demonstrator in a 2-armed bandit problem with changing probabilities of a reward. Participants observe a demonstrator with high or low fluid intelligence. In some treatments they are aware of the intelligence score of the demonstrator and in others they are not. Low fluid intelligence individuals imitate the demonstrator more when her fluid intelligence is known than when it is not. Conversely, individuals with high fluid intelligence adjust their use of social information, as the observed behaviour changes, independently of the knowledge of the intelligence of the demonstrator. We provide evidence that intelligence determines how social and individual information is integrated in order to make choices in a changing uncertain environment.

The purpose of this study is to locate the Sinhala language in the South Asian linguistic area and the Indo-Aryan subgroup and to examine the concepts and ideas related to the origin and evolution of the Sinhala language. The data were collected using document analysis and analysed using content analysis. Building the relevant rationale by analysing and synthesising the results of previous studies is the study path followed here. According to the findings, it is evident that the identity of the Sinhala language is the result of a different evolutionary path than other languages belonging to the Indo-Aryan sub-group of the Indo-European language family. Since Sinhala began to exhibit its independent aspect before the beginning of the 'Modern Indo-Aryan languages', it is justified to consider the Sinhala language as a modern language that emerged from the contact of a Middle Indo-Aryan dialect with the Old Indo-Aryan languages and the languages belong to other language families such as Dravidian and Sino-Tibetan. Although Sanskrit and the language known as 'Pali' are recognised as the 'mother languages' of Sinhala, there is no evidence that Sinhala was initiated as a dialect of either Sanskrit or 'Pali'. According to the historical analysis, several Prakrit vernaculars led to the source of the Vedic language, which was developed and modified as Sanskrit during 5c. B.C. was isolated from the original mainland and developed independently on this island. The mixing of Prakrit dialects on this island and the contact with the languages of the early inhabitants of the island was the first step in the independent aspect of the Sinhala and the contact with the colloquial languages of other nations arrived here due to various socio-economic, political, and cultural relationships led to the preparation of the new face of Sinhala. The origin and evolution of the Sinhala language took place through admixture with other non-Dravidian languages - mainly Tamil, the primary language of the Dravidian language family. The reasons for finding a vocabulary similar to Sanskrit and Pali languages - which are considered to have evolved from them - are that the Prakrit verículars based on those languages are rooted in the source of Sinhala and the development of Sinhala language due to socio-cultural (educational and religious etc.) reasons similar to Sanskrit and Pali languages. It is clear that the use of loan words and a derived vocabulary and the influence of the grammatical features of those languages.

Over the last three decades, the application of evolutionary theory to the human sciences has shown remarkable growth. This growth has also been characterised by a ‘splitting’ process, with the emergence of distinct sub-disciplines, most notably: Human Behavioural Ecology (HBE), Evolutionary Psychology (EP) and studies of Cultural Evolution (CE). Multiple applications of evolutionary ideas to the human sciences are undoubtedly a good thing, demonstrating the usefulness of this approach to human affairs. Nevertheless, this fracture has been associated with considerable tension, a lack of integration, and sometimes outright conflict between researchers. In recent years however, there have been clear signs of hope that a synthesis of the human evolutionary behavioural sciences is underway. Here, we briefly review the history of the debate, both its theoretical and practical causes; then provide evidence that the field is currently becoming more integrated, as the traditional boundaries between sub-disciplines become blurred. This article constitutes the first paper under the new editorship of the Journal of Evolutionary Psychology, which aims to further this integration by explicitly providing a forum for integrated work.

Collection of airborne laser scanner data for forest inventory is becoming a common practice today. To reduce cost when laser data are collected over large areas, the flight altitude or flight speed may be increased, resulting in low pulse density laser data. The effect of using a different pulse density of laser data on estimation accuracy of forest variables was investigated at stand level. Laser data were acquired by the airborne TopEye system at a 1,200-ha forest area located in southern Sweden (58°30′N, 13°40′E). The 70 selected stands were dominated by Norway spruce [Picea abies (L.) Karst.] and Scots pine (Pinus sylvestris L.) with tree height in the range of 6–28 m (mean 19 m) and stem volume in the range of 30–620 m3 ha−1 (average 286 m3 ha−1). Regression analysis was used to establish empirical functions at stand level. The pulse density of laser data was reduced from 25,000 to 40 returns ha−1. By reducing the pulse density the root mean square error (RMSE) for the tree height and stem volume estimation increased from 0.7 to 1.8 m and from 13% to 29%, respectively. A substantial decrease in estimation accuracy of tree height and stem volume could be observed at pulse densities <80 returns ha−1 (corresponding to about 10 m between adjacent laser returns). The rapid increase in RMSE at these pulse densities could be explained by the less accurate classification of ground returns. With access to a high resolution digital elevation model the RMSE for the tree height and stem volume estimation increased from 0.7 to 1.1 m and from 13% to 23%, respectively, as an effect of the reduced pulse density. Even though the pulse density was reduced to several meters between adjacent laser returns, the estimation accuracies were equal to or better than those commonly obtained by using conventional forest inventory methods, e.g., aerial photo interpretation. This finding implies that low pulse density airborne laser scanner data could be cost efficient to use in inventory for estimation of forest variables at stand level.

Dimensional characterization of trees plays important roles in phytoremediation project of precision agriculture and environmental protection. The dimensional characterization can be evaluated by using UAV-based geomatic surveys. The work in this study applies low-cost UAV photogrammetry for tree height estimation, particularly for a phytoremediation project on contaminated soils. Two locations that had differing mean tree heights (7m and 4m) were used for the purpose of study. Three different UAV flights were carried out at 40m, 50m, and 60m altitudes in Area 1, an olive grove, and two different flights at 45m and 52m altitudes in Area 2, which has poplar species. The Structure from Motion (SfM) method, Vegetation Filter (VF), Digital Surface Models (DSMs), and Raster computational tool were used to process the UAV point clouds in order to produce Canopy Height Models (CHMs) for a local maximum driven extraction of tree height. Relatively, the results obtained from the tree height estimation experiment for the locations using UAV are higher than the results obtained using in-field measurement, thereby justifying the suitability of UAV photogrammetric data for tree height estimation.