Vascular Plant Flora of Stone Mountain Park, DeKalb County, Georgia, Based on Digitized Specimens in the University of Georgia Herbarium (GA)

Sphagnum tenerum var. virginicum, described from coastal Virginia and the Domin- ican Republic, differs from var. tenerum in having branches infascicles of 4-5 and pores consistently in the epidermal cells of stems. Sphagnum tenerum Sull. & Lesq. is common and wide-ranging along the Atlantic Coastal Plain of the eastern United States. It occurs less commonly in the uplands of North Carolina and Tennessee, and perhaps elsewhere inland, and as a rare disjunct in northern South America. The original collection was made by Leo Lesquereux in northern Alabama and issued in 1857 as no. 11 in the first edition of Sul- livant and Lesquereux's Musci Boreali-Americani. The locality and habitat data are skimpy: Ad ri- vulorum per montes Raccoon Alabamae. The Raccoon Mountains were designated as the Sand and Raccoon Mountains on older maps but now merely as the Sand Mountains. They comprise a much dissected plateau that is a spur of the greater Cumberland Plateau. The dissections create a series of more or less flat-topped mountains, with an average elevation of about 1,500 ft. above sea level, rising from a base of 300-400 ft. The soil layer is thin, and there is considerable exposure of sand- stone. The summers are hot and dry, but in late fall to spring there are seasonal rivulets. The Sand Mountains extend southwesterly from Dade Coun- ty, Georgia, through Dekalb and Marshall counties, Alabama, ending near Albertsville. The junior au- thor has collected in the area on several occasions and found Sphagnum tenerum to be rather plentiful in both Dekalb and Marshall counties. Lesquereux's route is uncertain, but he could have collected S. tenerum in either county. The species occurs on nearly all peaks in the area, generally on thin soil seasonally wet from seepage at margins of vegeta- tion mats forming islands on exposed sandstone and also, as Lesquereux noted, along rivulets. In Dekalb Co. S. tenerum occupies the edges of boggy habitats that support Sarracenia, Drosera, and other plants

Although several species of Lycopodium occur in abundance in certain areas of the eastern United States, these plants have been reported infrequently in Georgia. This is particularly true for Lycopodium complanatum var. flabelliforme, which, according to Dr. Wilbur II. Duncan, Curator of the University of Georgia Herbarium, has been reported from only ten of Georgia's 159 counties. In July, 1958, while on a botanizing expedition with Mr. Tom Jackson, of Rinehart College, I came across a small stand of L. complanatum var. flabelliforme in a wooded area at the baseof Georgia's famous monadnock, Stone Mountain, in Dekalb County. According to Dr. Duncan this is the first report of this species from Dekalb County, so this raises the total to 11 counties. More recently, on January 24 of this year, my father, Mr. Jim Hutto, accompanied me on a search for the grapefern Botrychium dissectum var. obliquzcm in a wooded area in Hall County about six miles south of Gainesville, Georgia. I had previously located an extensive growth of B. dissecturm in this locality in 1958 and I was interested in reexamining it. Several feet from the bank of a small stream running through the area I came across a patch of L. complanatum var. flabelliforme. A search of the area revealed that this small patch was all that was present. Dr. Duncan informs me that this is a new station, although he has previously reported this Lycopodium from Hall County from a station about six miles further south. It is my opinion that this and other species are probably more abundant in Georgia than we realize, particularly in the northern part of the state. With increased botanizing on the part of Georgia fern enthusiasts I believe the picture of the distribution will become more complete.

The vascular flora was studied and plant habitats described within an abandoned limestone quarry contiguous to Center Hill Dam in DeKalb County, central Tennessee, between 1986–2000. From seven diverse quarry habitats, 282 species in 203 genera and 81 families were documented. One hundred sixty-two species (57.4%) are DeKalb County distribution records. Sixty-four (22.7%) are exotic taxa including 22 Tennessee “severe threat” and “significant threat” invasive exotic pests. The known vascular flora includes five Pteridophytes, three Gymnosperms, and 273 Angiosperms (69 Monocots, 205 Dicots). Two state-listed species were discovered: Elymus svensonii and Liparis loeselii. Carex molesta is reported here as a new addition to the Tennessee flora.

-We studied the location and site selection of burrows of black iguana, Ctenosaura similis, at Palo Verde, Guanacaste, Costa Rica, in 1989 and 1990. Our study site included the environs of buildings as well as dry lowland deciduous forest. Iguana had burrows on earth banks, and in trees, logs and rocks with a higher frequency than occurred at randomly-selected points. Burrows differed from random points in having less cover over the entrance and within 1 m (both rock and vegetation) than did the random points. Temperatures at the burrow entrance were cooler than at the random points, although air temperatures did not differ significantly. We suggest that the use of burrows and selection of burrow sites by black iguanas may relate to predation pressures, thermal constraints and daily activity patterns. Descriptions of general habitat use are numerous for reptiles (e.g., Heatwole, 1977), although there are fewer descriptions of nest sites (see Van Devender and Howard, 1973; Burger and Zappalorti, 1986, for review) or of nonnesting burrows. Studies of site selection, whereby the authors have shown that the reptiles chose particular sites over all available sites, are even less common. Studies of selection require the location of a sufficient sample of nests or burrows for statistical analyses, and it is often difficult to find reptile nests or burrows. Nonetheless, nest site selection has been studied in alligators (Alligator mississippensis; Garrick and Lang, 1977), green iguanas (Iguana iguana; Rand, 1968), turtles (Burger and Montevecchi, 1975; 430 This content downloaded from 157.55.39.153 on Mon, 19 Sep 2016 04:52:46 UTC All use subject to http://about.jstor.org/terms BURROW SELECTION BY IGUANAS Ewert, 1976; Stoneburner and Richardson, 1981), and pine snakes (Pituophis melanoleucus; Burger and Zappalorti, 1986). Most reptiles do not regularly dig burrows for daily occupation (see Wright and Wright, 1957), although many reptiles use subterranean burrows dug by mammals. However some lizards (Rand and Dugan, 1983) and a snake (Carpenter, 1982; Burger et al., 1988) dig burrows for nests or for use throughout the year. Presumably the location of these burrows reflects evolutionary constraints such as predation pressures, conspecific competition, and thermoregulation. In this paper we examine burrow site selection in black iguana (Ctenosaura similis) at Palo Verde, Guanacaste, Costa Rica. We were interested in whether the same burrows were used from year to year, whether burrow site characteristics varied between years, and whether burrow sites differed from the available habitat. Black iguanas are large iguanid lizards ranging from southern Mexico to Panama. The flesh is edible, and they are often hunted for food (Fitch and Hackforth-Jones, 1983). They range in size from 20-50 cm snout-vent length (SVL), with overlap between the sexes, although males are generally larger (Fitch and Hackforth-Jones, 1983). METHODS AND STUDY AREA We studied black iguanas at the Palo Verde National Park in Guanacaste, northwestern Costa Rica, in March of 1989 and 1990. Palo Verde contains mainly tropical lowland deciduous forest that borders the extensive marshes of the Tempisque River. Further description of the study area can be found in Gill (1989) and Burger and Gochfeld (1991). In 1989 we surveyed the area immediately around the OTS biological station, the deciduous forest within 25 m of the road between the OTS station and the park headquarters (hereafter called the hacienda), and around the hacienda (1 km from the station). We searched by walking transects through each area in a manner that allowed us to see nearly all of the habitat. Burrows included all holes where iguanas lived, but did not include nests or spaces under buildings. We included only burrows actively being used by black iguanas. We used photographs and sketches from 1989 to locate the sites of all burrows for comparison with 1990. To compare overall habitat choice (bank, ground, flat rock, rock or trees, see Table 2) with available habitat, we used a table of random numbers to generate coordinates for a grid used to select points for the entire study site. General habitat types included bank (slope over 15?), ground (flat area), flat rock, rocks or boulders, and trees, logs or roots. We recorded specific site characteristics (see Table 3) from all burrows and from an equal number of random points in each of the three plac s (OTS station, deciduous forest, hacienda). Specific site characteristics recorded included: slope; substrate (ground, rock, bank); percent cover over the opening; percent vegetation cover within 1 m of the point (or burrow entrance estimated); percent rock within 1 m; percent overall cover (vegetation, trees, rock) within 1 m; distance to the closest burrow and closest tree; and temperature of the air and at the mouth of the burrow entrance or spot. We so recorded the height and width of the burrow entrance for all burrows whether they were dug or were in rock crevices. Random points were selected by using a table of random numbers to generate a compass direction and a distance (1 to 4 m) from each burrow. Thus, each random point was matched to a burrow site. We estimated iguana length by noting where an iguana started and ended on the habitat and then measuring this distance, and checked our estimates against iguanas of known length (N = 20 from marked individuals, r = 0.9, P < 0.001). We were usually only 1-3 m from the iguana, so the estimates were possible. In 1989 both general habitat type (bank, ground, flat rock, rock, logs) and specific site characteristics were recorded for random points and for burrow sites. In 1990 we recorded the general habitat types for burrows at the OTS station, hacienda, and forest in between, but we tripled the size of the areas searched in equal proportion to the three types. We also recorded some site characteristics, including location, height and width of burrow entrance, percent cover at the entrance, overall slope of area with burrow, slope at the burrow entrance, nearestneighbor distance, and the length of the burrow. Burrow lengths are minima because we had difficulty measuring beyond the bend if a burrow made a major turn. Indeed it was impossible to determine if the stick was at the end. We used Wilcox X2 tests to determine differences in the distribution of site characteristics between the burrows and random sites, and Contingency x2 tests to determine differences between the general habitat types of random sites and burrows in 1989 and 1990, and between the 1989 and 1990 burrow sites.

A study on identification and mapping of soil, water and vegetation of Hattikuni watershed in Yadagiri district was carried out using Google Earth and QGIS open-source software. The study area was located in North-Eastern dry Sone of Karnataka at 16˚ 51' 45" to 16˚ 59' 14" N latitude and 77˚ 9' 3" to 77˚ 20' 14" E longitude and elevation ranges from 436m to 622m above amsl. The study area falls under the Survey of India toposheet of E43X1 and E43X5 with an area of 138sq.km. Various Thematic maps were developed using Google Earth and QGIS such as DEM, slope, drainage, waterbodies, flow direction, aspect, hillshade, contour, vegetation and soil maps. From the study we found that there were in total of eleven waterbodies as per SOI toposheets (1960-61) and when compared with Google Earth, only eight waterbodies were found having that too with lesser water spread area as compared with toposheet. The soils in the area were classified into seven classes out of which the fine, mixed and lithic ustropepts occupies the maximum area followed by the rock/scrub land. By mapping of vegetation, we found that the total area coverage was 3456.95 ha which accounts to 24.99% of the total area. This study made a conclusion that Google Earth, QGIS and Toposheet can be used in combination for the mapping, identification and change detection of primary resources.

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000-2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000-2019), and year-on-year results were highly correlated (94-97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting.

We would like to introduce our recently developed systems for taking images of herbarium specimens and for the automatic extraction of data from specimen labels at the Herbarium of the Museum of Nature and Human Activities, Hyogo, Japan (HYO). Firstly, we designed a low-cost, but high-quality specimen imaging system for non-professional photographers to obtain images rapidly (Takano et al. 2019). Our system uses a mass-produced, mirrorless single-lens reflex (SLR) camera (SONY ILCE6300) with a zoom lens (Samyang Optics SYIO35AF-E35 mm F/2.8). We made a photo stand by ourselves to reduce costs. In addition, we have adopted an LED (light-emitting diode) lighting system with high color rendering. This imaging system has been introduced, with some improvements or adjustments for available space, to various herbaria in Japan (e.g., University of Tokyo (TI), Kyoto University (KYO)), contributing to the digitization of herbarium specimens across Japan. Next, we developed a system to extract label information from specimen images. The specimen image was uploaded to Google OCR and data were extracted in the form of text. Uploading the whole specimen image decreased the reading accuracy of the software because the plant images behaved as OCR (Optical Character Reader) noise. Therefore, the label part was cut out from the whole specimen image by using D-Lib*1 and uploaded to tesseract OCR*2 for OCR extraction of the label information (Aoki 2019, Takano et al. 2020). When installing this system for HYO, we designed it as an application accessible externally via the internet, which proved very useful during the coronavirus pandemic: part-time workers checked and conducted label data input from home. Finally, we decided to develop a system that would automatically label the text data extracted by OCR and input them into the appropriate cells of the database. Even though the text data could be extracted from specimen images, it needed a human to input them into the database. Therefore, we adopted Named Entity Recognition (NER), a system that extracts named entities such as place names, identifying proper nouns from unstructured text data. It enables information recorded in herbarium specimens to be tagged as named entities. We tried text matching at first, but the result was not satisfactory, so we started to use machine learning instead. We compared three natural language libraries for Japanese: BERT (Bidirectional Encoder Representations from Transformers), Albert (A Lite version of BERT), and SpaCy. Despite BERT and SpaCy returning similarly high f-scores (indicating good performance), we decided to use SpaCy because it runs better on ordinary PCs or servers. With sufficient machine learning after the creation of a text corpus (a specialised dataset) specific to labels on herbarium specimens, we successfully developed the application. The project files are available on GitHub*3 (Takano et al. 2024). We then examined whether this system could be applied to non-plant specimen images, i.e., fishes or birds, and found that it could efficiently extract data. Therefore, we decided to publicize this system on the cloud server and share it with other natural history museums in Japan*4. Curators can obtain a unique ID and password and upload specimen images from their collection to extract label data. The digitization of natural history collections in Japan has been long behind other countries, and this system will help to accelerate it. The system mentioned above is specialized for the natural history collections of Japan, but we believe it is possible to build similar programs in other countries, and we hope our experience will contribute to the mobilization of the world’s natural history collections.

Digitisation of specimens at the Royal Botanic Garden Edinburgh (RBGE) has created nearly half a million imaged specimens. With data entry from the specimen labels on herbarium sheets identified as the rate-limiting step in the digitisation workflow, the majority of specimens are databased with minimal data (filing name and geographical region), leaving a need to add further label data (collector, collecting locality, collection date etc.) to make the specimens research ready. We are exploring a number of different ways to complete data entry for specimens that have been imaged. These have included Optical Character Recognition (OCR), to identify meaningful specimen groupings to increase the speed of data entry and more recently citizen science platforms to provide accurate crowd-sourced transcriptions of specimen label data. We sent specimen images of the Australian flowering plants held at RBGE herbarium to DigiVol (https://volunteer.ala.org.au/institution/index/21309224), the citizen science platform developed alongside The Atlas of Living Australia. In 29 expeditions, 156 citizen scientists completed collection label data entry for RBGE’s 41,000 specimens of Australian flowering plants. We found that 95% of the transcriptions were completed by less than a third (27%) of the volunteers. Of the four volunteer experience levels in DigiVol we found that the middle two, Collection Managers and Scientists, transcribed fewer specimens, but also made fewer mistakes. We found that by removing the filing name from the information provided with the expedition the number of errors in the Museum Details section of the transcription decreased, as the filing name was often added as the label name, regardless of whether this is the case. The feedback we provided for each expedition was used to highlight common errors to try and reduce their occurrence as well as to inform the volunteers of what their transcriptions had revealed about this part of the collection. We explore the citizen science transcription workflow, its rate-limiting steps and how we have worked to include the citizen science and OCR data on our online herbarium catalogue.

During a brief malacological survey, a total of 16 snail species were recorded (four mangrove amphibious, 12 terrestrial). For all the species new localities with GPS coordinates and habitat data are provided. The species Thapsia insulsa Preston, 1910 (Urocyclidae) is a new record to the island.

The research is devoted to an urgent modern problem: the identification of temperature factors that limit the distribution and survival of plants in the mountainous conditions of the Northern Urals. The article’s aim is to determine the air temperature in four altitudinal zones of the southern part of the Northern Urals (Sverdlovsk region 59º30´N, 59º15´E) and to identify regression relationships of the obtained temperature data with control temperature data from the nearest meteorological station. Registration of air temperatures was carried out from May to September 2019 around the clock, every two hours in the mountain forest zone (at an altitude of 460 and 640 m above sea level) under the canopy of Siberian stone pine forests, in the zone of subalpine woodlands with elements of mountain forest tundra (820 m above sea level) and on a plateau in the mountain tundra zone (1030 m above sea level). It has been established that the change in air temperature at different altitude levels and at the nearest meteorological station (far from 60 km, at an altitude of 202 m above sea level) occurs relatively synchronously. Difference between average daily temperatures at altitudes of 460, 640, 820 and 1030 m above sea level and the control data of the meteorological station is 2.2, 3.0, 4.7 and 5.1ºC respectively. For all altitude levels, a reliable close straight-line relationship between average daily air temperatures and meteorological station data has been established. The altitudes of 460, 640, 820 and 1030 m above sea level correspond to the coefficients of determination ( R 2) equal to 0.96, 0.95, 0.92 and 0.88. The relationship of the minimum temperatures of the corresponding altitude levels with the control data is also quite high ( R 2 is not lower than 0.7). With the help of the identified relationships and the obtained regression equations, it is possible to retrospectively restore the dynamics of the thermal regime according to the meteorological station data for mountain habitats of different altitudes in the southern part of the Northern Urals over a long period. Including extreme critical temperatures, which act as factors limiting the resettlement and survival of plants and determine the ecosystem biodiversity.

Bringing collections out of the dark

Small mammals of riparian communities in Iowa were studied during the summer using liveand snap-trapping techniques. Six general habitat types were identified from the herbaceous vegetation on 28 study plots selected to represent a range of habitats from open fields to deciduous forest. Predominant habitat alterations were grazing, timber removal, and stream-channel realignment. Small-mammal species diversity was highest in channelized habitats and lowest in dry floodplains. An index of breadth of habitat usage was calculated for 9 species of mammals; white-footed mice (Peromyscus leucopus) exhibited the most generalized habitat usage. With the use of stepwise multiple regression, relationships were determined between small-mammal species abundances and 12 variables describing microhabitat features. In many instances, small-mammal numbers also were correlated significantly with each other. The potential effects of 6 habitat alterations on the 9 small-mammal species are predicted. J. WILDL. MANAGE. 44(1):16-24 Suitable habitat probably is the most important factor influencing the distribution and abundance of small mammals within their geographic ranges (Baker 1968:101, Vaughan 1972:250-256). Some small-mammal species have specific habitat requirements and consequently are limited in their distribution, whereas others occupy a wide variety of habitats (Kaufman and Fleharty 1974, Kirkland and Griffin 1974, Briese and Smith 1975, Miller and Getz 1976). Reports of the general habitats occupied by small mammals are common in the literature, but few studies have quantified the factors within a locality that influence a species abundance. Recently, small-mammal abundance and distribution have been related to several measures of habitat structure (M'Closkey 1975, M'Closkey and Fieldwick 1975, M'Closkey and Lajoie 1975, Dueser and Shugart 1978, Holbrook 1978). Habitat disturbances such as streamchannel realignment (Possardt and Dodge 1978), clear-cutting (Kirkland 1977, Martell and Radvanyi 1977), fire (Krefting and Ahlgren 1974, Fala 1975), and strip mining (Verts 1959, DeCapita and Bookhout 1975) can affect small-mammal populations and alter community composition. Regardless of the nature of disturbance, if vegetation is changed and habitat is altered, populations of some species may benefit while others are affected adversely. The objectives of our study were (a) to determine habitat preferences of some small mammals and the factors influencing their abundance; and (b) to quantify the effects of habitat alterations, particularly stream-channel realignment and grazing, on community composition and species abundance. Although this study was conducted in southwestern Iowa, the results are applicable to other riparian communities, especially those with similar small-mammal communities. We thank the people of Guthrie County for allowing us to sample small mammals on their property. A. Brackney and D. Schlapkohl assisted in the field, and 1 Journal Paper J-9463 of the Iowa Agriculture and Home Economics Experiment Station, Ames. Project 2085, funded by the U.S. Fish and Wildlife Service, Office of Biological Services, National Stream Alteration Team. 2 Present Address: 9025 W. Herbert, Milwaukee, WI 53225. 16 J. Wildl. Manage. 44(1):1980 This content downloaded from 157.55.39.243 on Thu, 06 Oct 2016 04:44:14 UTC All use subject to http://about.jstor.org/terms HABITAT SELECTION BY SMALL MAMMALS * Geier and Best 17 D. F. Cox and M. Hand provided help with statistical analyses. Vegetation data were collected by J. P. Vogler. R. B. Dahlgren, W. L. Franklin, and R. Q. Landers reviewed an earlier draft of the

The North American Distributions of the Circumboreal Species of Splachnum and Tetraplodon

Tropical rainforest habitats are the most biologically diverse and spatially heterogeneous landscapes on earth, including a bewildering variety of life forms organized into a complex three-dimensional lattice. Sumatran forests contain enormous numbers of plant species, some of which are present at low densities, and the dominant tree species vary with elevation, soil structure, other landscape features, and geographic region (Whitten et al. 2000). Natural and anthropogenic disturbances produce a diversity of microhabitats within each general habitat type. Plant dispersal dynamics, interactions among plant species or individuals within rainforest habitats, and plant-animal interactions may also result in uneven distributions of plants across space (Condit et al. 2000; Silva and Tabarelli 2001). Therefore, each unit of area in a rainforest habitat contains a set of plants that differs at least slightly from that in the adjacent area. Accordingly, for territorial animal species that utilize plant resources, the actual and relative availability of specific plant foods in a given month may vary substantially between territories, even among neighboring groups.

Habitat use by birds was studied at Barrow, Alaska, during the 1975–1978 postbreeding seasons, when birds moved from tundra breeding areas to coastal areas to feed prior to migration. Principal component analysis revealed three general habitat types used by birds: gravel beach, mudflat, and slough edge. Of four common shorebirds, red phalaropes (Phalaropus fulicaria) usually occurred along gravel beaches, semipalmated sandpipers (Calidris pusilla) and western sandpipers (Calidris mauri) occurred mostly on mudflats and slough edges, and dunlins (Calidris alpina) had an intermediate distribution. Considerable variation in habitat distribution existed among years, and similarity of distributions among species within years suggested that groups of species were influenced by common environmental factors. Gravel beaches were generally distinct from slough edges and mudflats, and two major groups of eight species each were found mainly on mudflats and slough edges or on gravel beach transects. However, habitat use by many species varied considerably among years, suggesting that birds assess local conditions within relatively small areas within habitats. Most species preferred either gravel beaches or mudflats and slough edges, but that may be modified by environmental factors including food availability.