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Dataset used in "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis"

Please see the paper "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis" how the images were captured, manual counting was performed, training datasets were prepared and models were trained.

opencc-zeroSep 2022View details →

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Data from: Grazing by non-native ungulates negatively impacts vegetation important to a native species of concern

Non-native grazers compete with native species across the globe. In the northwestern Great Basin of the western United States competition among livestock, feral horses, and Greater Sage-grouse has been the subject of numerous legal actions and management policies, yet spatially explicit temporal data documenting the details of this competition are lacking. We present a novel approach to studying the composition of the herbaceous understory across three study areas within the Great Basin with different historic and contemporary grazing regimes. We surveyed the landscape using distance sampling for livestock and horse feces as an index of use. In addition, we surveyed the herbaceous understory of random sites as well as sites chosen by female Greater Sage-grouse to nest and brood their chicks. We used a novel Bayesian hierarchical modeling framework to link vegetation metrics with the spatial-temporal distribution of horses and livestock while accounting for observation error. When livestock and feral horses were not present, we found that Greater Sage-grouse chose sites with higher percentages of perennial grasses and forbs to build their nests and brood their chicks compared to what was available to them. As livestock increased, we found evidence for decreases in the percentage of perennial grasses, forbs, cheatgrass (Bromus tectorum), and increases in the amount of bare ground. These effects were consistent at available sites and brood sites, however, we found less evidence for an impact of livestock at nest sites. As feral horses increased, we observed similar results at available sites, but at sites chosen by females to nest and brood their chicks, we observed increases in the amount of invasive cheatgrass as feral horses increased, which could reflect attempts by Greater Sage-grouse to compensate for reductions in protective cover. We present a noninvasive approach to assess space use that can be applied to other species. More importantly, we document that grazing by non-native ungulates impacts components of the plant community important to Greater Sage-grouse reproduction. We provide spatial-temporal maps of livestock and feral horse use to aid managers attempting to balance the needs of livestock producers, feral horses, Greater Sage-grouse, and ecosystem function.

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Vegetation and vantage point influence visibility across diverse ecosystems: implications for animal ecology

Visual information can influence animal behavior and habitat use in diverse ways. Visibility is the property that relates 3D habitat structure to accessibility of visual information. Despite the importance of visibility in animal ecology, this property remains largely unstudied. Our objective was to assess how habitat structure from diverse environments and animal position within that structure can influence visibility. We gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest, shrub-steppe, prairie, and desert) to characterize viewsheds (i.e., estimates of visibility based on spatially explicit sightlines) from multiple vantage points. Both ecosystem-specific structure and animal position influenced potential viewsheds. Generally, as height of the vantage point above the ground increased, viewshed extent also increased, but the relationships were not linear. In low-structure ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds decreased as vantage points increased to heights above the vegetation canopy. In the forest, however, variation in viewsheds was highest at intermediate heights, and markedly lower at the lowest and highest vantage points. These patterns are likely linked to the amount, heterogeneity, and distribution of vegetation structure occluding sightlines. Our work is the first to apply a new method that can be used to estimate viewshed properties relevant to animals (i.e., viewshed extent and variability). We demonstrate that these properties differ across terrestrial landscapes in complex ways that likely influence many facets of animal ecology and behavior.

opencc-zeroSep 2022View details →

opencc-by-4.0Dec 2021View details →

Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique

Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth.

Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate

Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).

Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.

Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).

Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.

Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 2. Kaolin clay pit at hill Hasenberg in Wiesa, Saxony, Germany; view of southern high wall, showing deeply weathered late Early Miocene lignite seam by dark brown color in center (photographed 2015). Fossil-bearing strata were reported (e.g., Mai 1964) as below lignite seam, but this horizon does actually not crop out (also evidenced by new drillings, communicated by Dr. Jochen Rascher, GEOMONTAN GmbH company, Freiberg/Sa., Germany).

Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)

Text-fig. 1. Location of Wiesa fossil site in eastern Germany and other fossil sites for comparison. Explanation for map b: all fossil sites – black circles; grey circles – cities; topographic names in italics – German states (Länder). For bio- and lithostratigraphic data of fossil sites, see chapter Methodologies and material and Text-fig. 3.

Fig. 3 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile

Fig. 3. – Theodor Kotschy's Pflanzen Vertheilung [notes on distribution of duplicates] intended for Ludwig von KÖchel, 12 April 1842.

Fig. 1 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile

Fig. 1. – Order of the government of Mohammad Shah to support the work of Theodor Kotschy in Iran issued by the scribe Al-Seyed Osman, servant of the helm, c. 1843.

Fig. 6 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile

Fig. 6. – Lectotype of Aegilops kotschyi Boiss. (G-BOIS). Specimen collected in Iran, near Sabst-Buschom [Kuh-e Sabz Pushan] in May 1842 by Theodor Kotschy.

Fig. 4 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile

Fig. 4. – Theodor Kotschy's passport, issued 29 December 1835. [recto; © Archiv, Naturhistorisches Museum, Vienna]

Fig. 7 in Theodor Kotschy in Iran, 1841-1843. Botanical collections and an early printed vegetation profile

Fig. 7. – Rudolf Friedrich Hohenacker, HÖhenprofil und Kärtchen des südwestlichen Theiles von Persien. Esslingen, 1846. (Annotation in unknown hand). Detail of the HÖhenprofil is shown below.

opencc-by-4.0Sep 2017View details →

Correlation between noise and vegetation in urban areas

Analysis of correlation between vegetation and noise in urban areas. DOI article: 10.22541/au.150651402.23622693

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Climate is more influential to vegetation green-up than factors that contribute to erosion following high-severity wildfire

Background In the southwestern United States, post-fire vegetation recovery is increasingly variable in forest burned at high-severity. Many factors, including temperature, drought, and erosion, can reduce post-fire vegetation recovery rates. Here, we examined how post-fire precipitation variability, topography, and soils influenced post-fire vegetation recovery in the southwestern United States as measured by greenness. We modeled relationships between post-fire vegetation and these predictors using Random Forest and examined changes in post-fire normalized burn ratio across fires in Arizona and New Mexico. We incorporated growing season climate to determine if year-of-fire effects were persistent during the subsequent five years or if temperature, water deficit, and precipitation in the years following fire were more influential for vegetation greenness. Results We found reductions in post-fire greenness in areas burned at high-severity when heavy and intense precipitation fell on more erodible soils immediately post-fire. In <a>highly erodible</a> scenarios, when accounting for growing season climate, coefficient of variation for year-of-fire precipitation, total precipitation, and soil erodibility decreased greenness in the fifth year. While the effects of year-of-fire factors related to erosion were significant, they were small, and the variability explained by growing season vapor pressure deficit and growing season precipitation were significantly greater. Conclusions Our results suggest that while the factors that contribute to post-fire erosion and its effects on vegetation recovery are important, at a regional scale, the majority of the variability in post-fire greenness in high-severity burned areas in southwestern forests is due to climatic drivers such as growing season precipitation and vapor pressure deficit. Given the scale of area burned at high-severity, the likelihood that high-severity burned area will continue to increase, and the potential for more post-fire erosion that can result in different vegetation trajectories, quantifying how these factors alter the trajectory of greenness and what that means in terms of ecosystem development is central to understanding how different ecosystem types will be distributed across these landscapes with additional climate change.

opencc-zeroMay 2024View details →

opencc-by-4.0May 2024View details →

Potential Natural Vegetation of Eastern Africa (Burundi, Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia): raster and vector GIS files for each country

The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia. The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate. Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fspecies.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aeHdnF4n19CbTTznMdObr91vfZys%2FY1PrK1OxI%2BHif0%3D&reserved=0">https://vegetationmap4africa.org/species.html</a>) The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fvegetation_map.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VKVkjZ8lTKMyoU9luZLAYFDwY5sbwDrGXceVEQAeGIQ%3D&reserved=0">https://vegetationmap4africa.org/vegetation_map.html</a>) and for PDF versions of the map, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocumentation.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=FIsoem3dYG4%2FIQFMPlM8B2Vf9Doqf2CS7p2fevpAwx0%3D&reserved=0">https://vegetationmap4africa.org/documentation.html</a>). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocs%2Fversionhistory%2F&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=J1aJt1D0dUhDd2fF9uEo8k1uu%2F7josYZCnQG%2FXWj5Ks%3D&reserved=0">https://vegetationmap4africa.org/docs/versionhistory/</a>). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level. When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a>>. The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a>> A simplified version of the map can be found on Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.

opencc-by-4.0May 2015View details →

The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty. in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA

The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty.