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Figure 24 in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 24. Effects of pteroid depression. A, left lateral view of Anhanguera as in Fig. 23. B, as in (A), but with the left pteroid depressed by 30°. The camber is increased and the angle of attack, a, is reduced by this movement.

Figure 21. A in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 21. A, typical plots of pitching moment M against angle of attack a (measured with respect to the zero-lift angle of attack) for stable and unstable aircraft. Both aircraft are shown balancing at the same equilibrium angle of attack, at which point the pitching moment is zero. B, the effect of raising the elevators (1) on the longitudinal balance of a stable aircraft: the pitching moment is increased, as is the equilibrium angle of attack, so the aircraft pitches up (2) until equilibrium is restored. C, the effect of raising the elevators (1) on the longitudinal balance of an unstable aircraft: the pitching moment is increased, but the equilibrium angle of attack is reduced, so the aircraft now pitches up away from equilibrium (2).

Figure 15. A in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 15. A, reconstructed articular surfaces of the right carpometacarpal joint of Coloborhynchus robustus. Elements are oriented as in Fig. 7: distal syncarpal in lateral view and wing metacarpal in medial view. Scale bar: 50 mm. B, diagrammatic representation of (A), showing contact areas in the close-packed position and the joint axis. For a list of anatomical/arthrological abbreviations, see Appendix 1.

Figure 22 in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 22. Increasing the equilibrium angle of attack of an unstable aircraft can be brought about by decreasing the derivative dM/da, where M is the pitching moment and a is the angle of attack, by sweeping the wings back (A), or by decreasing the zero-lift pitching moment M0, by depressing the pteroids for example (B). These adjustments can theoretically be used in response to an unstable nose-up pitch (1), thus establishing a new equilibrium (2).

Figure 19. A in Three-dimensional geometry of a pterosaur wing skeleton, and its implications for aerial and terrestrial locomotion

Figure 19. A, Stable but unbalanced wing profile, with the centre of gravity (c.g.) situated ahead of the mean aerodynamic centre (m.a.c.). B, stable and balanced configuration, with a small tailplane set at a negative incidence with respect to the main wing.

opencc-zeroMay 2022View details →

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Data from: Phylogenetic relatedness drives protists assembly in marine and terrestrial environments

Aim: Assembly of protists communities is known to be driven mainly by environmental filtering, but the imprint of phylogenetic relatedness is unknown. In this study, we aim to test the degree at which co-occurrences and co-exclusions of protists in different phylogenetic relatedness classes are deviating from random expectation in two ecosystems in order to link them to ecological processes. Location: Global open-oceans and Neotropical rainforest soils Major taxa: Protists Time period: 2009-2013 Methods: Protist metabarcoding data originated from two large scale studies. Co-occurrence and co-exclusion networks were constructed using a recent method combining a null distribution model with Spearman's rank correlation coefficients among pairs of OTU. Phylogenetic relatedness was estimated using either global pairwise sequence distance or phylogenetic distance inferred from best maximum-likelihood trees derived from multiple alignments of OTU representative sequences. Significance of observed patterns relating networks and phylogenies were evaluated by distance classes against two null models in which either the tips of the phylogenetic trees or the network edges were randomized. Results: Closely-related protists co-occurred more often than expected by chance in all datasets, but also co-excluded less often than expected by chance in the marine dataset only. Concurrent excess of co-occurrences and co-exclusions were observed at intermediate phylogenetic distances in the marine dataset. Main conclusions: This suggest that environmental filtering and dispersal limitation are the dominant forces driving protists co-occurrences in both environments, while signal of competitive exclusion was only detected in the marine environment. Co-exclusion differences are potentially linked to the individual environments: marine waters are more homogeneous, while the rainforest soils contain a myriad of nutrient rich micro-environment reducing the strength of mutual exclusion.

FIG. 4 in Terrestrial macro-arthropods of the sub-Antarctic islands of Possession (Crozet Archipelago) and Kerguelen: inventory of native and non-native species

FIG. 4. — Some indigeneous arthropod species: Diptera: A, Telmatogeton amphibius (Eaton, 1875); B, Amalopteryx maritima Eaton, 1875; Hymenoptera: C, Kleidotoma icarus (Quinlan, 1964); Coleoptera: D, Antarctotachinus crozetensis Enderlein, 1909; Hemiptera: E, Phthirocoris antarcticus Enderlein, 1904; Lepidoptera: F, Pringleophaga kerguelensis Enderlein, 1905. Photos: Bernard Chaubet.

FIG. 1 in Terrestrial macro-arthropods of the sub-Antarctic islands of Possession (Crozet Archipelago) and Kerguelen: inventory of native and non-native species

FIG. 1. — The South Indian Ocean sub-Antarctic islands, with details of Kerguelen Island (A) and Possession Island (B, in Crozet archipelago). Modified from D. Fourcy, Inrae, 2020.

FIG. 3 in Terrestrial macro-arthropods of the sub-Antarctic islands of Possession (Crozet Archipelago) and Kerguelen: inventory of native and non-native species

FIG. 3. — Some indigeneous arthropod species: Arachnida: A, Hahnia crozetensis Hickman, 1939; B, Myro paucispinosus Berland, 1947; Coleptera: C, Ectemnorhinus viridis G. R. Waterhouse, 1853; D, Palirhoeus eatoni (C. O. Waterhouse, 1876); E, Amblystogenium pacificum (Putzeys, 1869); F, Meropathus chuni Enderlein, 1901; Diptera: G, Calycopteryx moseleyi Eaton, 1875; H, Anatalanta aptera Eaton, 1875. Photos: Bernard Chaubet.

FIG. 2 in Terrestrial macro-arthropods of the sub-Antarctic islands of Possession (Crozet Archipelago) and Kerguelen: inventory of native and non-native species

FIG. 2. — Some typical habitats of sub-Antarctic islands: A, wide open valley; B, wet coastal cliffs; C, slopes covered with Acaena magellanica; D, fell field; E, native vegetation; F, introduced vegetation; G, coastal slopes and foreshore; H, marine animal colonies. Photos: Maurice Hullé.

opencc-zeroOct 2021View details →

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The R package enerscape: A general energy landscape framework for terrestrial movement ecology

<ol> <li> Ecological processes and biodiversity patterns are strongly affected by how animals move through the landscape. However, it remains challenging to predict animal movement and space use. Here we present our new R package enerscape to quantify and predict animal movement in real landscapes based on energy expenditure. </li> <li> Enerscape integrates a general locomotory model for terrestrial animals with GIS tools in order to map energy costs of movement in a given environment, resulting in energy landscapes that reflect how energy expenditures may shape habitat use. Enerscape only requires topographic data (elevation) and the body mass of the studied animal. To illustrate the potential of enerscape, we analyze the energy landscape for the Marsican bear (Ursus arctos marsicanus) in a protected area in central Italy in order to identify least-cost paths and high-connectivity areas with low energy costs of travel. </li> <li> Enerscape allowed us to identify travel routes for the bear that minimize energy costs of movement and regions that have high landscape connectivity based on movement efficiency, highlighting potential corridors. It also identifies areas where high energy costs may prevent movement and dispersal, potentially exacerbating human-wildlife conflicts in the park. A major strength of enerscape is that it requires only widely available topographic and body size data. As such, enerscape permits a first cost-effective way to estimate landscape use and movement corridors even when telemetry data is not readily available, such as for the example with the bear. </li> <li> Enerscape is built in a modular way and other movement modes and ecosystem types can be implemented when appropriate locomotory models are available. In summary, enerscape is a new general tool that quantifies, using minimal and widely available data, the energy costs of moving through a landscape. This can clarify how and why animals move in real landscapes and inform practical conservation and restoration decisions. </li> </ol>

opencc-by-4.0Dec 1971View details →

Verteilung der gefangenen Individuen von Bombus terrestris und B. lucorum auf die einzelnen Fanggebiete in den Jahren 1959—1969. in Die Veränderung der Hummelfauna Cuxhavens in diesem Jahrhundert. Der Versuch einer Deutung. )

Verteilung der gefangenen Individuen von Bombus terrestris und B. lucorum auf die einzelnen Fanggebiete in den Jahren 1959—1969.

opencc-by-4.0Nov 2022View details →

Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)

This dataset was used for the analysis of the following publication: Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted) Any use of this dataset should cite the paper above (Creative Commons Attribution 4.0 International Public License). Contact: kim.calders@ugent.be   ================================================                     Dataset ================================================ General:  TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included [ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb] Full details of the methods to segment individual trees and generate the QSMs can be found in the paper Calders et al. Ecological Solutions and Evidence. Tree ID: Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base. Datasets: 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx 2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat  3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345   ================================================                     Paper analysis ================================================ We have provided all scripts (analysis_and_figures) that were used to: 1 ) analyse the data (TLS_Inventory.ipynb): ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees) ----- Link with census &1.4ha > trees_summary.csv (#835 trees) 2) generate the paper figures: ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/   ================================================                     Funding ================================================ The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).

opencc-by-4.0Mar 2022View details →

Meteorological responses of carbon dioxide and methane fluxes in the terrestrial and aquatic ecosystems of a subarctic landscape [Data set]

The data set contains carbon dioxide (CO2) and methane (CH4) fluxes of boreal subarctic landscape and its ecosystems and ecotones, and ancillary meteorological and environmental data, measured at Kaamanen, northern Finland (69°8’ N, 27°16’ E; 155 m a.s.l.), during June 2017 - June 2019. The studied ecosystems and ecotones include: upland pine forest, fen, treed pine bog, sparsely treed pine bog, lakes and string top fen plant community. C_fluxes1b_Heiskanen_et_al_2022.csv includes quality screened, u* filtered and gap-filled eddy covariance ecosystem flux data and modelled pine bog and string top time series utilising eddy covariance and manual flux chamber measurements. C_fluxes2_Heiskanen_et_al_2022.csv includes quality screened daily average lake fluxes from mineral and organic sediment lakes. environmental_data_Heiskanen_et_al_2022.xlsx includes ancillary meteorological and environmental data.

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Convergent adaptation of true crabs (Decapoda: Brachyura) to a gradient of terrestrial environments

For much of terrestrial biodiversity, the evolutionary pathways of adaptation from marine ancestors are poorly understood, and have usually been viewed as a binary trait. True crabs, the decapod crustacean infraorder Brachyura, comprise over 7,600 species representing a striking diversity of morphology and ecology, including repeated adaptation to non-marine habitats. Here, we reconstruct the evolutionary history of Brachyura using new and published sequences of 10 genes for 344 tips spanning 88 of 109 families. Using 36 newly vetted fossil calibrations, we infer that brachyurans most likely diverged in the Triassic, with family-level splits in the late Cretaceous and early Paleogene. By contrast, the root age is underestimated with automated sampling of 328 fossil occurrences explicitly incorporated into the tree prior, suggesting such models are a poor fit under heterogeneous fossil preservation. We apply recently defined trait-by-environment associations to classify a gradient of transitions from marine to terrestrial lifestyles. We estimate that crabs left the marine environment at least seven and up to 17 times convergently, and returned to the sea from non-marine environments at least twice. Although the most highly terrestrial- and many freshwater-adapted crabs are concentrated in Thoracotremata, Bayesian threshold models of ancestral state reconstruction fail to identify shifts to higher terrestrial grades due to the degree of underlying change required. Lineages throughout our tree inhabit intertidal and marginal marine environments, corroborating the inference that the early stages of terrestrial adaptation have a lower threshold to evolve. Our framework and extensive new fossil and natural history datasets will enable future comparisons of non-marine adaptation at the morphological and molecular level. Crabs provide an important window into the early processes of adaptation to novel environments, and different degrees of evolutionary constraint that might help predict these pathways. --

opencc-zeroDec 2022View details →

Fig. 2. A–C in The genus Eluma in the Iberian Peninsula, with the description of a new terrestrial isopod: Eluma matae sp. nov. (Oniscidea, Armadillidiidae, Eluminae)

Fig. 2. A–C. Eluma caelata (Miers, 1877). A. Habitus, lateral view (INV16000). B. Pereopod 7 male, ischium (JC56). C. Exopod I male (JC56). – D–F. E. tuberculata Cruz, 1991. D. Habitus, lateral view (JC605). E. Pereopod 7 male, ischium (INV16031). F. Exopod I male (INV16013). Scale bars: A, D = 1 mm; B–C, E–F = 0.1 mm.

Fig. 1 in The genus Eluma in the Iberian Peninsula, with the description of a new terrestrial isopod: Eluma matae sp. nov. (Oniscidea, Armadillidiidae, Eluminae)

Fig. 1. Map of location of the Iberian Peninsula and maps with the geographic distribution of the species of Eluma (bibliographical references with red triangle; material studied with blue triangle). A. E. caelata (Miers, 1877). B. E. matae sp. nov. C. E. tuberculata Cruz, 1991.

Fig. 4 in The genus Eluma in the Iberian Peninsula, with the description of a new terrestrial isopod: Eluma matae sp. nov. (Oniscidea, Armadillidiidae, Eluminae)

Fig. 4. Eluma matae sp. nov. A–F, I, K–Q. Paratype, ♂ (INV16006). J. Paratype, ♀ (INV16007). A–F. Scale setae. G. E. caelata (Miers, 1877), ♂ (CCZ-UGR852), scale setae. H. E. tuberculata Cruz, 1991, ♀ (INV16004), scale setae. I. Cephalon and first pereonite, frontal view. J. Antennule.K. Antenna. L. Pereopod 1. M. Pereopod 7. N. Endopod I. O. Exopod I. P. Endopod II. Q. Endopod II.

Fig. 3 in The genus Eluma in the Iberian Peninsula, with the description of a new terrestrial isopod: Eluma matae sp. nov. (Oniscidea, Armadillidiidae, Eluminae)

Fig. 3. Eluma matae sp. nov., paratype, ♂ (INV16006). A. Habitus, side view. B. Cephalon and first pereonite, frontal view. C. First pereonite, side view. D. Pleon, telson and uropods. E. Dorsal tegument and scale-setae. F. Scale-setae, detail. G. Carpus of the pereopod 1. H. Carpus of the pereopod 1, setae. I. Pereopod 7, ischium. J. Tip of endopod I. K. Exopod I. L. Exopod II. Scale bars: A = 1 mm; B–D = 0,5 mm; E, F, H = 0.05 mm; G, I–L = 0.1 mm.