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Fig. 3 in Eudiaptomus transylvanicus and E. vulgaris (Copepoda: Calanoida: Diaptomidae): comparative morphology, distribution and ecology

Fig. 3. Eudiaptomus vulgaris (Schmeil, 1898), female. a – habitus, ventral view; b – genital compound somite; c – rostrum; d – mandible; e – coxa of leg 5; f – exopod 1 and endopod of leg 5; g – leg 5; h – exopods 2 and 3 of leg 5; i – endopod of leg 2, with Schmeilsche lobus. Scale bars: 0.5 mm (a), 100 µm (b), 50 µm (c, e, f, g, h, i), 20 µm (d).

Fig. 9 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 9. Main types of variations of tail colouration of males in Saxicola maura variegatus. A, 5 to 10 mm; B, 11 to 20 mm; C, 21 mm and more.

Fig. 8 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 8. Adult males in breeding plumage, ventral (A–C) and dorsal (D–E) view. A, D, Saxicola maurus armenicus, coll. No. 28292/109, Azerbaijan, Nakhichevan Autonomous Republic, Dzul'fa Distr., NW slopes of Ilan-Dag Mt., 39°08.78′ N, 45°40.47′ E, 1,160 m a.s.l., 12 June 1974, Yu.A. Volnenko leg. (NMNH); B, E, Saxicola maurus variegatus, coll. No. 28005/106, Azerbaijan, Ismailli Distr., vicinity of Ismailly, 40°46.99′ N, 48°06.73′ E, 540 m a.s.l., 2 July 1973, V.M. Loskot leg. (NMNH); C, F, S. m. variegatus, coll. No. 173387/208-2002, Russia, Rostov Prov., Don River Delta, floodplain at mouth of Aksay River, Starodon'e Lake, 47°17.00′ N, 40°15.10′ E, 1 m a.s.l., 2 May 1997, G.B. Bakhtadze leg. (ZIN).

Fig. 7 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 7. Subadult females in spring plumage, ventral (A, B) and dorsal (C, D) view. A, C, Saxicola maurus armenicus, coll. No. 136211, Iraq, Wasit Governorate, Bagsaya ruins, 32°53.73′ N, 46°27.60′ E, 95 m a.s.l., 17 March 1914, P.V. Nesterov leg. (ZIN); B, D, Saxicola maurus variegatus, coll. No. 136212, the same locality, collector and collection, 16 March 1914.

Fig. 4 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 4. First year male of Saxicola rubicola rubicola in fresh autumnal plumage. Holotype of Saxicola torquata amaliae Buturlin, 1929. ZMMU, coll. No. R-13488, Russia, Republic Severnaya Osetiya – Alaniya, vicinity of Vladikavkaz, 42°59.99′ N, 44°38.53′ E, 750 m a.s.l., 13 Oct. 1919, L.B. Beme leg. Ventral (A) and dorsal (B) view.

Fig. 5 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 5. Females in fresh autumnal plumage, ventral (A, B) and dorsal (C, D) view. A, C, Saxicola maurus armenicus, coll. No. 136219, Iran, West Azerbaijan Prov., Vezne River valley, 36°34.51′ N, 45°10.80′ E, 1,400 m a.s.l., 18 July 1914, ad., P.V. Nesterov leg. (ZIN); B, D, Saxicola maurus variegatus, coll. No. R-97651, Russia, Krasnodar Terr., vicinity of Krasnodar, 45°16.60′ N, 38°05.39′ E, 3 m a.s.l., 10 Aug. 1973, 1-st year, A.M. Peklo leg. (ZMMU).

opencc-by-4.0Mar 2021View details →

Fig. 9. Cornucistela serrata, distribution. 1 in New data on diagnostics and distribution of the little-known comb-clawed beetle Cornucistela serrata (Coleoptera: Tenebrionidae: Alleculinae)

Fig. 9. Cornucistela serrata, distribution. 1, Wadi Khumra (holotype); 2, Heith (paratypes); 3, Kushm al-Buway- biyat (paratypes); 4, Quai'iya (specimen collected by Philby).

Fig. 2 in Eudiaptomus transylvanicus and E. vulgaris (Copepoda: Calanoida: Diaptomidae): comparative morphology, distribution and ecology

Fig. 2. Eudiaptomus transylvanicus (Daday, 1891), male. a – habitus, ventral view; b, b′ – right antennule; c – rostrum; d – leg 5; e – basis of right leg 5; f – exopod 2 of right leg 5; g – endopod of right leg 5; h – exopod 2 of left leg 5. Scale bars: 500 µm (a), 200 µm (b), 100 µm (b′, c, d), 40 µm (e), 30 µm (f), 20 µm (g, h).

Fig. 1 in Eudiaptomus transylvanicus and E. vulgaris (Copepoda: Calanoida: Diaptomidae): comparative morphology, distribution and ecology

Fig. 1. Eudiaptomus transylvanicus (Daday, 1891), female. a – habitus, lateral view; b – genital compound somite; c, c′ – mandible; d – rostrum; e – endopod of leg 2, with Schmeilsche lobus; f – leg 5; g – exopods 2 and 3 of leg 5; h – endopod of leg 5. Scale bars: 0.5 mm (a), 200 µm (b), 50 µm (c, e), 40 µm (d, g), 10 µm (c′, f, h).

Fig. 4 in Eudiaptomus transylvanicus and E. vulgaris (Copepoda: Calanoida: Diaptomidae): comparative morphology, distribution and ecology

Fig. 4. Eudiaptomus vulgaris (Schmeil, 1898), male. a – habitus, lateral view; b – ultimate segments of right antennule; c – leg 5; d – right leg 5 (coxa and basis) and left leg 5; e – rostrum. Scale bars: 0.5 mm (a), 200 µm (c), 300 µm (b), 50 µm (d, e).

Fig. 3 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 3. Males in fresh autumnal plumage, ventral (A–C) and dorsal (D–F) view. A, D, Saxicola maurus armenicus, coll. No. 136248, Iran, West Azerbaijan Prov., Vezne River valley, 36°34.51′ N, 45°10.80′ E, 1,400 m a.s.l., 22 July 1914, ad., P.V. Nesterov leg. (ZIN); B–F, Saxicola maurus variegatus, Georgia, Kakhetiya, vicinity of Lagodekhi, 41°48.28′ N, 46°16.56′ E, 380 m a.s.l., L.A. Portenko leg. (ZIN): coll. No. 163316/425- 974, 19 Sept. 1953, ad. (B, E) and coll. No. 163315/425-974, 21 Sept. 1953, 1-st year (C, F).

Fig. 2 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region

Fig. 2. Birds in nesting plumage. A, Saxicola maurus armenicus, coll. No. 28126/108, Azerbaijan, Nakhichevan Autonomous Republic, Dzul'fa Distr., NW slopes of Ilan-Dag Mt., 39°08.78′ N, 45°40.47′ E, 1,160 m a.s.l., 12 June 1974, female, Yu.A. Volnenko leg. (NMNH); B, Saxicola maurus variegatus, coll. No. 137879, Russia, Kabardino-Balkar Republic, Prokhladnyy (= Prokhladnaya Vill.), 43°45.11′ N, 44°05.79′ E, 180 m a.s.l., 2 July 1883, male, K.N. Rossikov leg. (ZIN).

opencc-by-4.0Nov 2023View details →

Dataset from: Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows

This is the dataset for an article published in Marine Environmental Research titled, 'Tolerance to aerial exposure influences distributional patterns in multi-species intertidal seagrass meadows', in October 2023. Following is the abstract for the paper for which this was the primary data:Multi-specific seagrass meadow assemblages dominate most tropical intertidal regions but the relative role of environmental stress in determining distribution patterns is still uncertain. Here we combine observational and experimental approaches to examine aerial exposure as a factor driving species occurrence patterns in intertidal meadows of the Andaman archipelago, where up to 6 seagrass species co-occur. In the studied meadow, patterns of exposure did not map onto distance from the coast, instead creating a patchy matrix of exposure, based on fine-scale bathymetric differences. Distributional surveys showed that seagrass species were similarly patchy, often tracking the degree of aerial exposure during low tide. While some species (Halophila ovalis, Halophila minor, and Thalassia hemprichii) frequently occurred in submerged or subtidal areas and were rarely found in completely exposed areas, other species (Cymodocea rotundata, Halophila beccarii, and Halodule uninervis) also occupied areas that were subject to partial or complete aerial exposure during low tide. To confirm this pattern, we used field-based transplant experiments, employing a natural gradient of tidal exposure to subject six seagrass species to different desiccation exposure times. After a month, H. beccarii and H. uninervis transplants survived in areas that sustained more than 3 h of aerial tidal exposure without significant mortality, compared with other species (H. ovalis, H. minor, T. hemprichii, C. rotundata) that showed dramatic shoot mortality at the same exposure regimes. For all species, 4 h represented the upper limit of exposure, in both experimental and distributional studies. However, despite their wider tolerance of exposure to air, H. beccarii and H. uninervis did not dominate the entire meadow. This could be a result either of their poor tolerance to other environmental factors or their lower competitive abilities among other mechanisms. This suggests that in tropical multi-specific meadows, strong environmental filters could override clear intertidal zonation to create patchy matrices based on species tolerances.

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Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics

Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem-specific hydraulic conductivity (KS), water potential at leaf turgor loss (ΨTLP), and water potential at 50% loss of KS (ΨP50) varied along climate gradients. The ΨTLP and ΨP50 increased with climatic moisture only for evergreen species, but KS did not. Species with high ΨTLP and ΨP50 values were associated with both dry and wet environments. However, drought-deciduous species showed high ΨTLP and ΨP50 values regardless of water availability whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half-life. These results suggest that environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics.

opencc-zeroDec 2022View details →

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Code and data for Bayesian joint species distribution model selection for community-level prediction

Code and data for reproducing the analysis in the manuscript "Bayesian joint species distribution model selection for community-level prediction." Provided data include percent cover observations for 39 modeled vascular plant species within boreal forest understory communities and environmental model covariates. R code is provided to generate model inputs, apply alternative models, generate out-of-sample predictions, and calculate associated community and species log scores and alternative model evaluation metrics. Further, R source code is provided to implement the multinomial joint species distribution model defined in the manuscript. Details on the data, its processing, and the alternative model definitions and structure can be found in the main text of the manuscript. Provided data are currently being used in ongoing analyses and coordination with authors may be warranted to avoid duplicate publication. Potential users are encouraged to consider collaboration with authors when useful and appropriate. Misinterpretation of data may occur if used outside the context of the original analysis. All data are made available in their current state. While significant efforts have been made to ensure data accuracy, complete accuracy cannot be guaranteed. Data may be updated periodically. It is the responsibility of the data user to check for updated versions of the data.

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Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions

Aim Species distribution models (SDMs) that integrate presence-only and presence-absence data offer a promising avenue to improve information on species' geographic distributions. The use of such 'integrated SDMs' on a species range-wide extent has been constrained by the often-limited presence-absence data and by the heterogeneous sampling of the presence-only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map-based evaluation. We build a new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs. Location South and Central America. Time period 1979-2017. Major taxa studied Hummingbirds. Methods We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: i) cross-validation with presence-absence data and ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set. Results The integrated SDM accounting for the spatially varying sampling intensity of the presence-only data was one of the top-performing models in both model validation schemes. Presence-only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence-only data for species that had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction. Main conclusions Integrated SDMs combining presence-only and presence-absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence-absence data.

FIG. 7 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 7. — Dendrogram of floristic similarity of the bryophyte flora of mangroves on the Northern and Southeastern coast of Brazil.

FIG. 5. — A in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 5. — A, mean richness; B, density of bryophytes in the sampled mangroves per light tolerance guilds; C, interaction plot between sampled zones and light tolerance guilds on mean richness of bryophytes; D, interaction plot between sampled zones and light tolerance guilds on mean density of bryophytes.

FIG. 4 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 4. — Violin plot with included boxplot: A, species richness; B, species density. Alpha-diversity indices: C, Shannon Index (H'); D, Pielou's Evenness (J').

FIG. 3 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 3. — Accumulation curves based on the abundance of individuals in the fringe and inland zones of the mangroves of Salvaterra, Pará, Brazil: A, species richness (q = 0); B, Shannon diversity (q = 1). The fringe zone is shown in red color and the inland zone in blue color. Continuous line represents interpolation and dotted line represents extrapolation.