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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.

FIG. 2 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 2. — Mangroves on the east coast of the municipality of Salvaterra, Marajó Island, Pará: A, B, mangrove in inland zone; C, D, fringe zone.

FIG. 1 in Spatial Distribution and Substrate Preferences of Bryophyte Species in Mangrove Ecosystems of the East Coast of Marajó Island, Brazil

FIG. 1. — Location map of collection points in Marajó Island, Pará, Brazilian Amazon:A, localization of Marajó Island in Pará, Brazil, South America (red rectangle); B, localization of Salvaterra in Marajó Island; C, localization of sampling points on the east coast of the Salvaterra, with 1 km between the fringe zone and the inland zone in each area (map prepared by P.W.P. Gomes).

opencc-by-4.0Nov 2023View details →

Figs 11–19 in A New Species Of The Planthopper Genus Polychornum Gnezdilov, 2021 (Hemiptera: Caliscelidae: Ommatidiotinae) Extends The Distribution Of The Genus And Tribe Augilini Baker To Africa

Figs 11–19. Polychornum centroafricanum sp. n., holotype, male genitalia: 11 = genital block, lateral view; 12 = lower margin of pygofer, ventral view; 13 = anal tube, lateral view; 14 = anal tube, dorsal view; 15 = penis, dorsal view; 16 = penis, ventral view; 17 = penis, lateral

opencc-by-4.0Nov 2023View details →

Figs 6–10 in A New Species Of The Planthopper Genus Polychornum Gnezdilov, 2021 (Hemiptera: Caliscelidae: Ommatidiotinae) Extends The Distribution Of The Genus And Tribe Augilini Baker To Africa

Figs 6–10. Polychornum centroafricanum sp. n., holotype: 6 = head, frontal view; 7 = head, lateral view; 8 = head, dorsal view; 9 = right forewing; 10 = apex of left forewing. Not to scale

opencc-zeroJan 2024View details →

Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions

Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.

opencc-zeroJan 2024View details →

Do food distribution and competitor density affect agonistic behaviour within and between clans in a high fission-fusion species?

Socioecological theory attributes social variation in female-bonded species to differences in within- and between-group competition, shaped by food distribution. Strong between-group contests are expected over large, monopolisable resources, but not when low-quality food is distributed across large, undefended home ranges. Within-group contests are expected to be more frequent with increasing heterogeneity in feeding sites. We tested these predictions in female Asian elephants, which show traits associated with infrequent contests – predominant graminivory, overlapping home ranges, and high fission-fusion. We examined how agonistic interactions within and between female elephant clans (social groupings) vary with food distribution and competitor density. We found stronger between-clan contests than that known from neighbouring forests and more frequent agonism between females between clans than within clans. Such strong between-clan contest is attributable to food patchiness as the Kabini grassland in the study area had three times the grass biomass as adjacent forests. Within-clan agonism was also frequent but was not influenced by food distribution, contradicting socioecological predictions. Contrary to recent claims, increasing within-clan agonism with group (party) size showed that ecological constraints operate despite high fission-fusion in Asian elephants. Thus, despite graminivory and fission-fusion, within-clan and between-clan agonism can be frequent, especially at high population density.

opencc-zeroFeb 2024View details →

Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions

Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.

opencc-zeroMar 2024View details →

Resources for: Spatio-temporal integrated Bayesian species distribution models reveal lack of broad relationships between traits and range shifts

Aim: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest for conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends. Location: Tanzania, East Africa. Time period: 1980-1999 and 2000-2020. Major taxa studied: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders. Methods: We applied recently developed integrated spatio-temporal species distribution models in R-INLA, combining citizen science and bird atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure-related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non-climatic pressures over recent decades. We correlated patterns of change with species traits. Results: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species-specific results. Main conclusions: We highlight the importance of individual assessments, while demonstrating the usefulness of our analytical approach for analyses of range shifts.

Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 in A revision of the genus Armillipora Quate (Diptera: Psychodidae) with the descriptions of two new species

Fig. 8. Heat map resulting from the Species Distribution Model using MaxEnt, where 1 is equal to the highest probability of distribution, while 0 is the lowest probability.

Fig. 12. Distribution records for E in Taxonomic revision of the oil-collecting bee subgenus Epicharis (Epicharitides) Moure, 1945 (Hymenoptera: Apidae), with the description of two new species

Fig. 12. Distribution records for E. lia sp. nov., E. mesoamericana sp. nov. and E. rufescens Moure & Seabra, 1959.

Fig. 10. Distribution records for Epicharis cockerelli Friese, 1900, E. duckei Friese, 1901 and E. iheringi Friese, 1899. Symbols with a in Taxonomic revision of the oil-collecting bee subgenus Epicharis (Epicharitides) Moure, 1945 (Hymenoptera: Apidae), with the description of two new species

Fig. 10. Distribution records for Epicharis cockerelli Friese, 1900, E. duckei Friese, 1901 and E. iheringi Friese, 1899. Symbols with a cross represent records from literature.

opencc-zeroMar 2024View details →

FIG. 2 in Paleotropical distribution of the genus Neotropicomus A.C.Magnago, Alves-Silva & T.W.Henkel: a new species from India

FIG. 2. — Phylogram inferred from combined ITS and 28S dataset using IQTree program. BS ≥ 60% is indicated above or below the branches. Novel taxon is given in bold.

opencc-zeroMar 2024View details →

FIG. 1 in Paleotropical distribution of the genus Neotropicomus A.C.Magnago, Alves-Silva & T.W.Henkel: a new species from India

FIG. 1. — Neotropicomus indicus sp. nov. (from holotype): A-C, basidiomata; D, basidiospores; E, basidia; F, G, pleurocystidia; H, cheilocystidia; I, pileipellis; J, stipitipellis; K, terminal cells of pileipellis; L, caulocystidium. Scale bars: A-C, 20 mm; D-H, K, L, 10 μm; I, J, 20 μm.

Fig. 21 in Four new species and five new distribution records of the jumping spider genus Stenaelurillus Simon, 1886 (Salticidae: Aelurillines) from India

Fig. 21. Stenaelurillus tamravarni Marathe & Maddison, 2022, male habitus, live photographs from Chandrappa circle, Bangalore, Karnataka. Photographs by Amith Kiran Menezes.

Fig. 20 in Four new species and five new distribution records of the jumping spider genus Stenaelurillus Simon, 1886 (Salticidae: Aelurillines) from India

Fig. 20. Stenaelurillus neyyar Sudhin, Sen & Caleb, 2023, habitus, live photographs from Nagercoil, Tamil Nadu. A–C. Specimen, ♂ (NRC-AA-6968). D–F. Specimen, ♂ (NRC-AA-6969). G–I. Specimen, ♀ (NRC-AA-6970). Photographs by Rishikesh Tripathi.

Fig. 18 in Four new species and five new distribution records of the jumping spider genus Stenaelurillus Simon, 1886 (Salticidae: Aelurillines) from India

Fig. 18. Stenaelurillus gabrieli Prajapati, Murthappa, Sankaran & Sebastian, 2016, habitus, live photographs from Kudal, Sindhudurg, Maharashtra. A–D. Specimen, ♀ (NRC-AA-6965). E–G. Specimen, ♂ (NRC-AA-6964). Photographs by Rishikesh Tripathi.

Fig. 19. Stenaelurillus lesserti Reimoser, 1934 in Four new species and five new distribution records of the jumping spider genus Stenaelurillus Simon, 1886 (Salticidae: Aelurillines) from India

Fig. 19. Stenaelurillus lesserti Reimoser, 1934., habitus, live photographs from Bengaluru (A–B, G) and Puducherry (C–F). A–B, D, F. Specimen, ♂ (NRC-AA-6966). C, E. Specimen, ♀ (NRC-AA-6967). G. Mating. Photographs by Amith Kiran Menezes (A–B, G) and Rishikesh Tripathi (C–F).