JOTT January 2016

ISSN 0974-7907 (Online)ISSN 0974-7893 (Print)

Date of Publication: 26 January 2016 (Online & Print) www.threatenedtaxa.org

January 2015 | Vol. 8 | No. 1 | Pages: 83098420DOI: 10.11609/jott.2016.8.1.8309-8420

Taxa Journal ofThreatenedOPEN ACCESS

EDITORSFounder & Chief EditorDr. Sanjay Molur, Coimbatore, India

Managing EditorMr. B. Ravichandran, Coimbatore, India

Associate EditorsDr. B.A. Daniel, Coimbatore, India Dr. Ulrike Streicher, Wildlife Veterinarian, Danang, Vietnam Ms. Priyanka Iyer, Coimbatore, IndiaDr. Manju Siliwal, Dehra Dun, IndiaDr. Meena Venkataraman, Mumbai, India

Editorial AdvisorsMs. Sally Walker, Coimbatore, India Dr. Robert C. Lacy, Minnesota, USA Dr. Russel Mittermeier, Virginia, USA Dr. Thomas Husband, Rhode Island, USA Dr. Jacob V. Cheeran, Thrissur, India Prof. Dr. Mewa Singh, Mysuru, IndiaMr. Stephen D. Nash, Stony Brook, USA Dr. Fred Pluthero, Toronto, Canada Dr. Martin Fisher, Cambridge, UK Dr. Ulf Grdenfors, Uppsala, Sweden Dr. John Fellowes, Hong Kong Dr. Philip S. Miller, Minnesota, USA Prof. Dr. Mirco Sol, Brazil

Editorial Board Subject Editors 20142015A. Biju Kumar, University of Kerala, Thiruvananthapuram, IndiaA.J. Solomon Raju, Andhra University, Visakhapatnam, IndiaAlbert G. Orr, Griffith University, Nathan, AustraliaAlessandre Pereira Colavite, Universidade Federal da Paraba, BrazilAlexi Popov, National Museum of Natural History, Sofia, BulgariaAlexander Ereskovsky, IMBE, Marseille, FranceAndreas Khler, Universidade de Santa Cruz do, BrazilAngela R. Glatston, Rotterdam Zoo, The Netherlands. Anjana Silva, Rajarata University of Sri Lanka, Saliyapura, Sri LankaAnnemarie Ohler, Musum national dHistoire naturelle, Paris, FranceAnsie Dippenaar-Schoeman, University of Pretoria, Queenswood, South AfricaAntonio D. Brescovit, Instituto Butantan, BrasilAntonio A. Mignucci-Giannoni, Universidad Interamericana de Puerto Rico, Puerto RicoAnwaruddin Chowdhury, Guwahati, IndiaAparna Watve, Pune, Maharashtra, IndiaArthur Y.C. Chung, Sabah Forestry Department, Sandakan, Sabah, MalaysiaB.C. Choudhury (Retd.), Wildlife Institute of India, Dehradun, India. B. Ravi Prasad Rao, Sri Krishnadevaraya University, Anantpur, IndiaB. Shivaraju, Bengaluru, Karnataka, IndiaB.A. Daniel, Zoo Outreach Organization, Coimbatore, Tamil Nadu, India B.S. Kholia, Botanical Survey of India, Gangtok, Sikkim, IndiaBolvar R. Garcete-Barrett, FACEN, Universidad Nacional de Asuncin, ParaguayBrett C. Ratcliffe, University of Nebraska, Lincoln, USABrian Fisher, California Academy of Sciences, USAC. Raghunathan, Zoological Survey of India, Andaman and Nicobar IslandsC. Srinivasulu, Osmania University, Hyderabad, IndiaCarl Ferraris, Smithsonian Institution, Portland, USA

Ceclia Kierulff, Victorville , CaliforniaCecilia Volkmer Ribeiro, Porto Alegre, Brazil.Chris Bowden, Royal Society for the Protection of Birds, Sandy, UKChristoph Kueffer, Institute of Integrative Biology, Zrich, SwitzerlandChristoph Schwitzer, University of the West of England, Clifton, Bristol, BS8 3HAChristopher L. Jenkins, The Orianne Society, Athens, Georgia Cleofas Cervancia, Univ. of Philippines Los Baos College Laguna, PhilippinesColin Groves, Australian National University, Canberra, AustraliaCrawford Prentice, Nature Management Services, Jalan, MalaysiaC.T. Achuthankutty, Scientist-G (Retd.), CSIR-National Institute of Oceanography, Goa D.B. Bastawade, Maharashtra, IndiaD.J. Bhat, Retd. Professor, Goa University, Goa, IndiaDale R. Calder, Royal Ontaro Museum, Toronto, Ontario, CanadaDaniel Brito, Federal University of Gois, Goinia, BrazilDavid Mallon, Zoological Society of London, UKDavor Zanella, University of Zagreb, Zagreb, CroatiaDeepak Apte, Bombay Natural Hisotry Society, Mumbai, IndiaDiana Doan-Crider, Texas A&M University, Texas, USA Dietmar Zinner, German Primate Center, Gttingen, GermanyDunston P.Ambrose, St. Xaviers College, Palayamkottai, IndiaE. Vivekanandan, Central Marine Fisheries Research Institute, Kochi, IndiaEduard Vives, Museu de Cincies Naturals de Barcelona, Terrassa, SpainEric Smith, University of Texas, Arlington, USAErin Wessling, Max Planck Institute for Evolutionary Anthropology, GermanyF.B. Vincent Florens, University of Mauritius, MauritiusFerdinando Boero, Universit del Salento, Lecce, Italy Francesco Dal Grande, Senckenberg Gesellschaft fr Naturforschung, FrankfurtGeorge Mathew, Kerala Forest Research Institute, Peechi, IndiaGernot Vogel, Heidelberg, GermanyGiovanni Amori, CNR - Institute of Ecosystem Studies, Rome, Italy Gombobaatar Sundev, Professor of Ornithology, Ulaanbaatar, MongoliaG.P. Sinha, Botanical Survey of India, Allahabad, India Hari Balasubramanian, EcoAdvisors, Nova Scotia, CanadaHayrnisa Ba Sermenli, Mula University, Ktekli, Turkey H.C. Nagaveni, Institute of Wood Science and Technology, Bengaluru, IndiaH.C. Paulo Corgosinho, Bairro Universitrio, Frutal, BrazilH. Raghuram, The American College, Madurai, IndiaHeidi S. Riddle, Riddles Elephant and Wildlife Sanctuary, Arkansas, USAHem Sagar Baral, Charles Sturt University, NSW AustraliaHemant V. Ghate, Modern College, Pune, IndiaHeok Hee Ng, National University of Singapore, Science Drive, SingaporeHui Xiao, Chinese Academy of Sciences, Chaoyang, ChinaIan J. Kitching, Natural History Museum, Cromwell Road, UKIan Redmond, UNEP Convention on Migratory Species, Lansdown, UKIndraneil Das, Sarawak, MalaysiaIvana Karanovic, Hanyang University, Seoul, Korea J. Jerald Wilson, King Abdulaziz University, Jeddah, Saudi ArabiaJ.W. Duckworth, IUCN SSC, Bath, UKJack Tordoff, Critical Ecosystem Partnership Fund, Arlington, USAJames Young, Hong Kong Lepidopterists Society, Hong KongJeff McNeely, IUCN, Gland, SwitzerlandJesse Leland, Southern Cross University, New South Wales, AustraliaJill Pruetz, Iowa State University, Ames, USA Jim Sanderson, Small Wild Cat Conservation Foundation, Hartford, USA Jodi L. Sedlock, Lawrence University, Appleton, USA John C. Morse, Clemson University, Long Hall, Clemson, USAJohn Huber, Canadian National Collection of Insects, Ontario, Canada. John Noyes, Natural History Museum, London, UKJohn Veron, Coral Reef Foundation, Townsville, Australia

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continued on the back inside cover

Front cover: Water colour and ink rendering of Fishing Cat Prionailurus viverrinus by G. Moorthy, Pitchandikulam Forest, Auroville.

Habitat quantity of Red-cockaded Woodpecker Picoides borealis (Aves: Piciformes: Picidae) in its former historic landscape near the Big Thicket National Preserve, Texas, USA

Vivek Thapa 1 & Miguel F. Acevedo 2

1 313 Throckmorton Street, Gainesville, TX 76240, USA2 Department of Electrical Engineering and Institute of Applied Sciences, University of North Texas, 3940 North Elm Street, Denton, TX, USA1 [email protected] (corresponding author), 2 [email protected]

8309


OPEN ACCESS

Art

icleJournal of Threatened Taxa | www.threatenedtaxa.org | 26 January 2016 | 8(1): 83098322

DOI: http://dx.doi.org/10.11609/jott.1735.8.1.8309-8322

Editor: K.A. Subramanian, Zoological Survey of India, Kolkata, India. Date of publication: 26 January 2016 (online & print)

Manuscript details: Ms # 1735 | Received 31 December 2014 | Final received 10 January 2016 | Finally accepted 11 January 2016

Citation: Thapa V. & M.F. Acevedo (2016). Habitat quantity of Red-cockaded Woodpecker Picoides borealis (Aves: Piciformes: Picidae) in its former historic landscape near the Big Thicket National Preserve, Texas, USA. Journal of Threatened Taxa 8(1): 83098322; http://dx.doi.org/10.11609/jott.1735.8.1.8309-8322

Copyright: Thapa & Acevedo 2016. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use of this article in any medium, reproduc-tion and distribution by providing adequate credit to the authors and the source of publication.

Funding: U.S. National Science Foundation (NSF) for partial funding for this study under the grant CNH BCS-0216722.

Conflict of Interest: The authors declare no competing interests.

Author contribution: VT conducted all field work, performed GIS and remote sensing analysis, and wrote the manuscript. MFA was graduate professor for VT at the University of North Texas. He supervised all work of the study and the paper and performed sensitivity analysis.

Author Details: Vivek Thapa - Currently, he is working as a GIS Manager/CAD Drafter in a land surveying company called All American Surveying, located in North Central Texas, USA. He uses GIS, remote sensing, AutoCAD, to create maps for clients. Miguel F. Acevedo - in addition to his departmental affiliations he is Faculty in the Graduate Program in Environmental Sciences, University of North Texas. His work integrates environmental monitoring and modeling to understand the dynamics of environmental and ecological systems, and to provide socially relevant results concerning pollutants, land use change and climate variability.

Acknowledgments: We thank U.S. National Science Foundation (NSF) for partial funding for this study under the grant CNH BCS-0216722. We acknowledge the feedback and help from several University of North Texas faculty members during the project: Drs. Pete A.Y. Gunter, and J. Baird Callicott, Department of Philosophy and Religion Studies, and Dr. Pinliang Dong, Department of Geography.

Abstract: We quantified pine-forested habitat suitable for Red-cockaded Woodpecker Picoides borealis in the former historic range of the species to assess the potential for possible re-colonization. We used a remotely-sensed image and geographic information systems (GIS) to create a land-use/land (LU/LC) binary cover map, from which we calculated the habitat suitability index (HSI) based on an estimated home range of 50ha. A sensitivity analysis revealed the necessity for more data to make an accurate estimate, but our analysis of landscape metrics indicates more than 930ha of suitable habitat patches. These patches are heavily fragmented and mostly located on private lands. They can be assessed for understory and herbaceous vegetation and can be restored for possible re-establishment of approximately 18 groups/colonies of Red-cockaded Woodpeckers.

Keywords: Accuracy assessment, Big Thicket National Preserve, global positioning system, habitat fragmentation, habitat suitability, landsat image, metrics, patches.

Journal of Threatened Taxa | www.threatenedtaxa.org | 26 January 2016 | 8(1): 830983228310

Habitat quantity of Picoides borealis Thapa & Acevedo

INTRODUCTION

Red-cockaded Woodpecker Picoides borealis is Near Threatened (BirdLife International 2013) and nationally an endangered species (USFWS 2014). The bird is endemic to mature pine forests of the southeastern United States, which once extended from Florida to New Jersey and as far west as Texas, reaching inland to Oklahoma, Missouri, Kentucky and Tennessee (Ligon 1970; Jackson 1971; Ferral 1998; USFWS 2005). During the early 19th century the wide spread of agriculture and timber harvesting led to severe habitat degradation and substantially reduced the woodpecker habitat range, which is currently scattered north from Florida to Virginia and west to southeast Oklahoma and southeastern Texas. The species is no longer found in New Jersey, Maryland, Tennessee, Missouri and Kentucky, while in southeastern Texas birds are mostly found in national forests of Angelina, David Crockett, Sabine and Sam Houston, but not in Big Thicket National Preserve (Conner & Rudolph 1995). The drastic reduction of mature pine forests coupled with modern forestry practices such as a reduced timber-rotation period and fire suppression proved detrimental to woodpecker populations, and the species was listed as endangered in 1973 (Hooper et al. 1980; Conner & Rudolph 1989, 1991, 1995; Costa & Walker 1995).

Red-cockaded Woodpeckers are habitat specialists that require large, old and living species of Longleaf Pinus palustris, Shortleaf P. echinata, Loblolly P. taeda, Pond P. serotina and Slash P. elliotii pine, preferring Longleaf Pine for nesting and foraging (Hooper et al. 1980; Jackson 1994; Hedrick et al. 1998; Conner et al. 2004). The optimal tree age varies with species, i.e., 80100 years for loblolly and shortleaf pine and 100120 years for Longleaf Pine with enough heartwood space to support cavity chambers and little or no mid-story hardwood vegetation (Hooper et al. 1980; Conner et al. 1994; Hedrick et al. 1998). Natural or prescribed fires controlled the mid-story overgrowth for decades and the result was open, park-like mature pine woodlands and savannahs with abundant herbaceous ground cover that provided an ideal habitat for these birds. Besides age, the potential cavity tree has high rates of Red-heart Fungal Phellinus pini infection that softens the heartwood and facilitates cavity excavation (Conner et al. 1976, 1994, 2004; Conner & Locke 1982; Hooper 1988; Walters 1990).

A colony or cluster is a collection of two to >12 cavity trees in 510 acres (approximately 24 ha) of land, and the cavity trees are normally located within a one-mile

radius from each other (USFWS 2005). A single colony has two to nine birds, with one breeding pair and the rest helpers. A suitable foraging habitat or territory surrounds a colony and covers an area of 30 to 81 contiguous hectares (75200 acres) of park-like mature pine stands (Hooper et al. 1982; Jackson 1994). Thus only contiguous open stands of mature longleaf and other pine species with herbaceous ground cover offer high quality habitats for Red-cockaded Woodpeckers (Conner & Rudolph 1991).

Few studies exist on the use of geographic information systems (GIS) and remote sensing to study the habitat of Red-cockaded Woodpeckers. Thomlinson (1993) used GIS, remote sensing and landscape ecology to study ecological characters of suitable pine stands in southeastern Texas. Cox et al. (2001) evaluated GIS methods that were used to assess Red-cockaded Woodpecker habitat and cluster characteristics. Ertep & Lee (1994) used GRASS to facilitate Red-cockaded Woodpecker management at Fort Benning Military Reservation. Another recent study by Santos et al. (2010) reports the use of remote sensing based on hyperspectral imagery to study tree senescence in Red-cockaded Woodpecker habitats. They used reflectance properties of the bands to detect senesced pine trees and found Red-cockaded Woodpeckers did not inhabit such trees. We utilized GIS and remote sensing techniques to study the spatial distribution of pine forest in one of the former historical ranges of Red-cockaded Woodpeckers (southeastern Texas) and to assess suitable habitats. We also used habitat suitability index (HSI) models and FRASGSTATS to evaluate or quantify species-habitat relationships. HSI models provide a quantitative measure of the quality of wildlife habitats and can integrate our understanding of wildlife-habitat relationships especially at landscape scales (Larson et al. 2003). In addition, process-oriented and empirical HSI models are commonly used to assess wildlife-habitat relationships (Dettki et al. 2003). Process-oriented models assess plausible causal relationships to provide a general conceptual framework; whereas empirical models analyze data on habitat characteristics collected at specific sites (Thapa et al. 2014).

For this paper we adopted a process-oriented approach to develop a heuristic HSI model for the Red-cockaded Woodpecker. This approach is based on a literature review (U.S. Fish and Wildlife Services HSI models), field observations (ground-truth) and geographic data obtained from topographic maps (scale 1:24000, USGS). An HSI is based on a set of functional relationships between habitat suitability (expressed as a

Journal of Threatened Taxa | www.threatenedtaxa.org | 26 January 2016 | 8(1): 83098322 8311


dimensionless index or score) and habitat requirements (variables). These variables are selected according to their relevance to the organism; for example herbaceous canopy cover, tree canopy cover, tree height, tree age and proximity to water. There is a partial suitability for each variable, which scales from 0 (unsuitable habitat) to 1 (optimum habitat). The overall HSI, which also scales from 0 to 1, is calculated with a formula that represents hypothetical relationships between partial suitability indices. GIS provides a tool to synthesize habitat data derived from remotely sensed sources together with databases of elevation, soil types, land use, and land cover. Thus GIS can be coupled with remote sensing to calculate HSI over relatively large geographic areas, and incorporate landscape variables at multiple spatial scales. We also demonstrate the use of GIS and remote sensing to collect or prepare data for habitat fragmentation study by using software called FRAGSTATS, which is a computational program designed to calculate a wide array of landscape metrics from categorical maps (McGarigal & Marks 1994, 1995; McGarigal 2002). Some of the metrics are commonly used to measure and quantify spatial patchiness in terms of composition (patch types and abundance) and configuration (shape and juxtaposition). These metrics represent the percentage of fragmented habitats, area of largest patch, andmost importantlythe area of remaining potentially suitable habitat (Girvetz et al. 2007).

In this paper, we used aforementioned habitat characteristics and applied remote sensing, GIS and FRAGSTATS techniques to examine abundance, distribution and fragmentation of available pine forest and provide a possible scenario for re-colonization by Red-cockaded Woodpeckers. We have four scientific objectives: (1) to use a Landsat Enhanced Thematic Mapper Plus (ETM+) image to develop a land-use/land-cover map (Laperriere et al. 1980); (2) to develop an heuristic GIS-based HSI model and a map for the woodpecker; (3) to determine the spatial distribution of current potentially suitable habitats; and, (4) to illustrate a general methodology for conservation cartography and spatial analysis that can be adapted to other interior-forest-dwelling avifauna of conservation interest. In addition, we have two policy-oriented objectives: (1) to provide a map of potentially suitable Red-cockaded Woodpecker habitat that may be preserved for (a) existing populations in the region or (b) that may serve as sites for establishing new populations in the region; and (2) to indicate the most important habitat characteristics, such as shape, size, and habitat

composition for purposes of proactive Red-cockaded Woodpecker habitat management in the region.

METHODS

Study AreaThe study area is located near the Gulf coastal plains

of southeastern Texas between the Trinity River to the west and the Neches River to the east, around the small towns of Kountze, Silsbee, Lumberton and suburbs north of Beaumont that adjoins the 39,338ha Big Thicket National Preserve (BTNP) (3031 0N to 9495 0W) (Fig. 1). Over the last five decades the landscape surrounding the BTNP has been converted from continuous pine forest to a matrix dominated by agriculture, pasture, timber plantations and exurban and suburban development (Wilcove et al. 1986). As a result the pine forests were converted into small patches isolated by a matrix of agricultural or other developed lands (Callicott et al. 2007). The study area was further subjected to intense oil and gas exploration that continues today. While such activities seem to have minimal effects on breeding, proximity to roads and vehicular movement does affect foraging activities of Red-cockaded Woodpeckers (Charles & Howard 1996). Annual precipitation averages 1350mm (Marks & Harcombe 1981; Callicott et al. 2007) and is uniformly distributed throughout the year, but because of its proximity to the Gulf of Mexico the Big Thicket study area experiences a high frequency of devastating tropical storms and hurricanes. Since 1900, 40 tropical storms and hurricanes have struck the Gulf coast, with Rita in 2005 and Ike in 2008 being the most recent big storms to hit Texas (NOAA 2008). However, these hurricanes did not cause damage in the study area as they did in the surrounding counties and areas especially near Galveston Bay, Harris and Angelina (NOAA 2005; Bainbridge et al. 2011). Nevertheless, hurricanes and other extreme natural disturbances such as severe winter can damage large portions of cavity and foraging trees, thereby affecting breeding populations of Red-cockaded Woodpeckers, which in turn leads to loss of genetic diversity (Reed et al. 1988, Bainbridge et al. 2011).

The vegetation types of the study area can be characterized by both community physiognomy and physiographic position. Forests, savannas, and shrub thickets are normally combined with important trees such as pine, oak, and other hardwoods to characterize community physiognomy while upland, slope, floodplain and flatland indicates the physiographic position of the



vegetation (Marks & Harcombe 1981). Thus, according to community physiognomy and physiographic position, four broad types of vegetation characterize the Big Thicket region. The upland forest community consists of dominant longleaf pine forest or mixed with a small-tree layer of Bluejack Oak Quercus incana. The slope community includes dominant species of shortleaf and loblolly pines with overstory hardwoods of Southern Red Oak Quercus flacata, White Oak Q. alba, Magnolia Magnolia grandiflora, and American Beech Fagus grandifolia. The floodplain vegetation consists of hardwood forests of European Hornbeams Caprinus sp., Sweetgum Liquidambar styraciflua and Water Oak Q. nigra mixed with Bald Cypress Taxodium distichum and Water Tupelo Nyssa aquatica and very few loblolly pines. And the flatlands include dominant species of Basket Oak Q. michauxii, Willow Oak Q. phellos, Laurel Oak Q. lauriflora, P. taeda and Red Ash Fraxinus pensylvanica. Data Acquisition and Image Processing

We selected a cloud-free Landsat ETM+ scene of March 2003 for analysis because the spring season was considered optimal for achieving the highest reflectance for floodplain hardwood forests and pine trees (Thomlinson 1993). The image was geographically referenced using ground control points (GCPs) created in ERDAS IMAGINE (a suite of software tools by Leica Geosystems Geospatial Imaging). The GCPs should be uniformly distributed over the image with good coverage

near the edges. At least, 16 GCPs are considered reasonable if each GCP can be located with an accuracy of one-third of a pixel size (Bernstein 1983). This number may not be sufficient, however, if the GCPs are poorly distributed or if the nature of the landscape prevents accurate placement (Campbell 1996). Following these guidelines, we extracted 19 GCPs from topographic maps to georectify the image. In addition, the coordinate system was modified into the Universal Transverse Mercator coordinate system (zone 15) and newly revised datum of the 1984 world geodetic system to correlate with the image. Landsat TM data were acquired from six spectral bands: TM1 (0.450.52 m), TM 2 (0.520.60 m), TM3 (0.630.69 m), TM4 (0.760.90 m), TM5 (1.551.75 m) and TM7 (2.082.35 m) (Luiz & Garcia 1997). Other data include topographic maps at a scale of 1:24,000; GIS files of roads and streets; polygons of towns; and aerial reconnaissance of 41 sections of the study area. More detailed data on vegetation were obtained by ground-truth (GPS points) visits to 287 sites. Individual global positioning system (GPS) points were accompanied with notes of soil texture, soil moisture regime, land use, plant composition, and elevation data. Digital cameras were used to take photographs of each site visited. ArcMap, a suite of GIS tools by Environmental Research Systems Inc. (ESRI), was used for GIS processing. A file with GPS points was imported to ArcMap as a shape file.

Figure 1. Study site - Portion of former historic habitat range of Red-cockaded woodpecker.



Field Data Collection and Vegetation ClassificationWe collected a total of 287 vegetation GPS points in

MayJune of 2007. We used a cloud-free Landsat image of 2003 to perform supervised classification procedures to derive final land use and land cover (LU/LC) categories. Supervised methods require the user to define the spectral characteristics of known areas of land-use types and develop training sites (Thapa et al. 2014). The training sites or signature is employed to verify and define distinct classes (Jenson 1996). This is achieved either by users prior knowledge of the geographic features of an area of interest such as identification of distinct, homogenous regions that represent each class (e.g., water or grass) or by ground-truth data such as GPS points, which refers to the acquisition of knowledge about the study area from field work, analysis of aerial photography, and from personal experience (Conner et al. 1975). Ground-truth data are considered to be the most accurate (true) data available about the area of study. They should be collected at the same time as the remotely sensed data, so the data corresponds as much as possible (Stars & Estes 1990). Furthermore, elements of visual interpretations such as color, shape, texture and pattern on aerial photos are commonly used that provides valuable clue during supervised classification. For example, we employed a texture and pattern analysis technique on aerial photos and selected pixels in such areas. With texture and pattern it is easy to differentiate naturally growing trees and human managed plantations, e.g., coconut and pine plantations. We derived seven LU/LC categories (water, urban areas, pine forest, pine plantation, mixed forest, grass, and cypress forest on floodplain) from the Landsat image (Fig. 2). We classified entire pixels into their designated classes according to the vegetation categories found in the study area. For example areas with tall pine trees were classified as pine trees, areas with mixed pine and oak trees were labeled as mixed forest, areas of floodplain were labeled as cypress trees on sloughs and so on. GPS locations of each category accompanied with aerial and field photos were extensively used during classification process.

Accuracy AssessmentIt is necessary to assess the accuracy of any thematic

classification to evaluate its intended application, and high accuracy assures consistency and reliability of derived landscape metrics (Xulong et al. 2005; Shao & Wu 2008). Several factors related to the sensors as well as to the classification process contribute to classification errors (Lunetta et al. 1991). It is also critical to measure

the quality and accuracy of data used for classification (Congalton & Green 1999). The classification or errors are analyzed by a confusion or error matrix, which is also called accuracy assessment (Congalton & Green 1999). An error matrix or accuracy assessment cell array is a table with entries representing the number of sample units; i.e., pixels, clusters of pixels, or polygons assigned to a specified class relative to the actual class found on the ground (Congalton 1991). Rows contain a list of class values for the pixels in the classified image file and columns represent class values for the corresponding reference pixels, determined by input from the user collected from sources such as aerial photographs, GPS points, previously tested maps or other data. The reference class values are compared with the classified image class values to assess the accuracy of an image classification. According to Anderson et al. (1976), classification accuracy close to 85% is acceptable for a LU/LC study.

Several statistical measures of a classified LU/LC map can be derived from an error matrix, including overall classification accuracy (sum of the diagonal elements divided by the total number of sample points), categorical omission and commission errors, and the KHAT coefficient (an index that measures the agreement between reference and classified data i.e., KHAT=1 when the agreement between reference and classified data reach 100%). A minimum of 204 reference points are required to achieve 85% accuracy with an allowable error of 5% (Jensen 1996). First we generated about 300 random (reference) points, and with help of aerial photos and with prior knowledge of geographic features we assigned values for each random point. Then we compared these reference class values with the classified image class values, which gave us an overall accuracy of 77.33% with KHAT = 0.7277. Then we used GPS locations as reference points and compared them with the class values of image files, which produced an overall accuracy of 81.48% with KHAT = 0.7449 (Table 1). The latter accuracy was deemed acceptable for this study because it was within the 5% allowable margin of error and was closer to 85%.

Habitat Suitability Index ModelsWe computed the HSI value for each pixel of the

resultant classified image according to the following procedure. We selected the pine trees class because this habitat is required for successful breeding and foraging. We assigned a value of 1 to this class and set all others to 0, producing a binary map showing pine trees only. We ran neighborhood analysis in ArcGIS, which is a statistic



that uses surrounding pixels in a defined neighborhood to assign a value to a target pixel. It is commonly used to find the most dominant land-cover category in a neighborhood or to find the number of certain LU/LC categories within a specified neighborhood. We selected neighborhood size based on the annual home range size of the Red-cockaded Woodpeckers as reported by several studies and determined using a variety of methodologies including 100%, 95%, 50% minimum convex polygons and 95% fixed kernel estimator (DeLotelle et al. 1987; Engstrom & Sanders 1997; Doster & James 1998; Walters et al. 2002; Douglas et al. 2008). According to Franzreb (2006), the minimum convex polygon estimator includes outliers or areas that are not used by the animals (birds in this case), and provides an overestimate of the home-range size making it less suitable as a descriptive statistic in terms of the biology of the species. On the other hand, the fixed kernel estimator is relatively insensitive to the presence of outliers and is less biased and produces more consistent results. Thus we selected the home-range size that was produced by fixed kernel estimator, i.e., ~50ha. To implement this, we selected a neighborhood size of 2323 pixels, i.e., 529 pixels of 900m2 each (3030 m), which yields a slightly lower value

of 476,100m2 or 47.61ha or 117.6 acres. A suitability index S for a pixel was determined as the proportion of pixels in pine tree cover in the neighborhood around the target pixel. Once all pixels are evaluated, we have an HSI map (Fig. 3).

Habitat Fragmentation and FRAGSTATSFRAGSTATS accepts Arc/Info (a GIS ESRI tool) polygon

files (vector) or raster (a matrix or grid of pixels) images in a variety of formats (McGarigal & Marks 1994). For our study we adopted the raster version of FRAGSTATS and used the HSI map as input data. Prior to this, the HSI map was reclassified into three discrete classes using an equal interval method, one of the several classification methods available in ArcMap. We selected this method because it groups the pixels according to their values and allowed us to maximize the difference amongst classes. A pixel was assigned a value of 1, 2, or 3, according to its index value. The index value for 1 was 0.000 to 0.333, 0.333 to 0.666 for 2 and 0.666 to 1 for 3. These three classes represent very unsuitable (1), unsuitable (2), and (3) potentially suitable habitat (Fig. 3). A suite of metrics was selected and computed at patch, class and landscape levels (Table 2). The resultant metrics reflected various

Figure 2. Seven land-use categories derived from supervised classification.



configurations and compositions of a landscape (Doster et al. 1998; Thapa et al. 2014).

RESULTS

Image ProcessingFrom a total of 287 GPS points, we used 162 points

for classification accuracy. The rest (125) were used to classify the image. Use of GPS points and visual interpretation of aerial photos proved effective in Landsat ETM+ classification and facilitated the process. Our results show pine trees and grass have the lowest classification accuracy with 74.58% and 75% respectively. For pine trees this might be due to insufficient GPS points, because we could not gather data from private land containing pure stands of old pine trees. For grass it is possible to include agricultural lands, a common problem with landsat images having 30m resolution. In addition, short-grass areas (grazed pastures or manicured lawns) and dirt roads had overlapping values with other urban areas such as patches of bare soil and asphalt roads. We classified pine plantations with 90.91% accuracy because they were easily identified based on texture and pattern on aerial photos and GPS data collected from within plantation areas. Similarly, we classified urban areas with 89.66% accuracy as they are also easily identifiable on aerial photos and GPS data. Water pixels were classified with 100% accuracy. And it is one of the geographic features that a user can accurately classify in remote sensing applications as water pixels exhibits the lowest reflectance property when examined in a spectral profile. Profile Tools of ERDAS allow the users to examine spectral behavior of pixels of different features. Cypress trees on sloughs

class was classified with 76.92% accuracy. Cypress trees occur mostly in sloughs of floodplains mixed with oak species and several factors such as topographic shadows and deep water contributed to the low accuracy of this class. Similar to water, wet sandy banks of creeks, streams, rivers, and sloughs have a lower reflectance in most bands and they became a source of confusion. Statistical analysis of spectral responses or profile from training samples, as well as ellipse and dendrogram plots, showed a similar reflectance with dark pixels (wet soils, black soil, and topographic shadows). Furthermore, vegetated (forest, urban, grass) and non-vegetated (water) were spectrally distinct. In order to redefine, refine and improve accuracy, we constantly reduced, merged and masked the confused classes.

Metrics at Patch, Class, and Landscape LevelsWe calculated two metrics i.e. area/density/edge

and connectivity of three different patch types (class) or habitat types: very unsuitable, unsuitable and potentially suitable (Table 2). These metrics were used to examine composition and configuration of patches in the study area (McGarigal et al. 2002). FRAGSTATS provides individual patch properties at three levels: patch, class and landscape, but we quantified patch properties at class level only because most metrics are redundant and provide similar values at patch and landscape levels. For example, total core area (TCA) at

Table 1. Combination of GPS and random points to assess classification accuracy

Class NameReference

TotalsClassified

TotalsNumberCorrect

Producers Accuracy, %

Users Accuracy, %

1. Grass 5 4 3 60.00 75.00

2. Pine plantation 13 11 10 76.92 90.91

3. Pine trees 53 59 44 83.02 74.58

4. Urban area 59 58 52 88.14 89.66

5. Cypress trees on sloughs 12 13 10 83.33 76.92

6. Water 1 1 1 100.00 100

7. Mixed forest 19 17 13 68.42 76.47

Totals 162 163 133

Overall Kappa Statistics 0.7449

Overall Accuracy, % 81.48

Area/Density/Edge MetricsCA/TA = Total Class Area (ha)PLAND = Percentage of Landscape (%)NP = Number of patches LPI = Largest Patch Index (%)

Connectivity MetricsCOHESION = Patch Cohesion Index

Table 2. Two selected FRAGSTATS metrics



class and landscape levels is defined the same as core area at patch level except the core area is aggregated over all patches of the corresponding patch type at the class and landscape levels. Since the selection of class breaks 0.333 and 0.666 was arbitrary, we performed sensitivity analysis by perturbing these nominal break values by 10% to examine the effect on the resultant habitat suitability indices (Table 3). We then used the manual classification method in ArcMap to create maps for visual inspection (Figs. 46). We used a total of nine break selections including the nominal values (Table 4). The perturbations above and below the nominal values effected the value of HSI as shown in Table 5.

Total (Class) Area (CA/TA) and Percentage of landscape (PLAND) metrics measure landscape composition (Table 5). CA measures how much of the landscape is occupied by a particular patch type. CA approaches 0 when a landscape consists of a single patch type, i.e. the landscape is not fragmented and CA > 1 indicates fragmentation of the landscape. For potentially suitable habitat patch, the value of CA/TA falls in three distinct classes, i.e., 0.5991, 0.6661 and 0.7331 for nine break selections and the amount of land occupied decreases with decreasing range of pixel values. For example, in

the total study area of 52,371.90ha, potentially suitable habitat reach 2,177.10ha when the break is 0.599 (Fig. 4), decreases to 933.48ha when the break is 0.666 (Fig. 5), and further decreases to 344.61ha when the break is 0.733 (Fig. 6) (Table 5). PLAND quantifies the proportional abundance of patch type in a landscape, i.e., similar to CA, PLAND approaches 0 when a landscape consists of single patch type. In other words, PLAND metrics largely mirror the patterns of CA. The PLAND metric revealed that potentially suitable habitat occupies 4.16% when the break is 0.599, decreases to 1.78% when the break is 0.666, and further decreases to 0.66% when the break is 0.733. Thus the amount of potentially suitable habitat area decreases with increasing break values and vice versa. Number of patches (NP) is a simple measure of the extent of subdivision or fragmentation of a patch type. NP = 1 when the landscape contains only a single patch type and NP > 1 indicates degree of fragmentation. NP for potentially suitable habitat varies from 28 to 67 and 72 as the break value changes from 0.599, 0.666 and 0.733, indicating that this habitat type is more fragmented than the unsuitable and very unsuitable types (Table 5). Largest Patch Index (LPI) is a simple measure of dominance as it quantifies the percentage

Figure 3. Habitat suitability index(HSI) map with three habitat types - very unsuitable, unsuitable, and potentially suitable.



of total landscape occupied by the largest patch. LPI = 0 when the largest patch of the corresponding patch type is small and 100 when the largest patch occupies the entire landscape. The largest patch of potentially suitable habitat occupies only 0.10.42 % of the landscape as compared to 3060 % and 1840 % for very unsuitable and unsuitable habitat types respectively. This corroborates the results of CA/TA and PLAND that showed presence of small amount of potentially habitat types as compared to the other two.

Connectivity is considered a vital element of landscape structure, and we used a single connectivity metric, COHESION, to observe physical connection between patches. COHESION = 0 when patches are less connected and approaches 100 when they are more connected. Our analysis showed the potential suitable habitat patch type is physically disconnected as indicated by 9396 as compared to the other two habitat types with sometimes approaching almost 100 showing they are more connected and contiguous.

DISCUSSION

Image ProcessingOverall, the LU/LC map derived from satellite imagery

was satisfactory because categories were adequately mapped and resulted only in minor misclassifications.

The resultant map was refined with spatial masking and recoding to achieve acceptable accuracy. Use of aerial photographs and GPS points proved effective in improving classification accuracy. The contrasting reflectance of bare areas and vegetation in the visible and infrared bands facilitated accurate identification. However, accurate delineation of grass from crops and shrubs represented a challenge (as in many remote sensing studies). Visual examination of the satellite imagery of the study area and field work revealed numerous dirt roads crisscrossing the entire landscape. Our study area once contained booming oil towns and clearly shows signs of human-induced fragmentation. Several pipelines, power lines and railroads cut through the study area, dissecting the landscape into smaller fragments.

GPS locations of different categories or classes proved to be the most critical data during LU/LC classification of the landsat image in facilitating and enhancing

Breaks -10% Nominal +10%

Unsuitable Breaks (UB) 0.3 0.333 0.366

Suitable Breaks (SB) 0.599 0.666 0.733

Table 3. Perturbation above and below the nominal break values Table 4. Nine break selections

Breaks Selection

Habitat Type

1 2 3

1 0-0.3 0.3-0.599 0.599-1

2 0-0.333 0.333-0.599 0.599-1

3 0-0.366 0.366-0.599 0.599-1

4 0-0.3 0.3-0.666 0.666-1

5 0-0.333 0.333-0.666 0.666-1

6 0-0.366 0.366-0.666 0.666-1

7 0-0.3 0.3-0.733 0.733-1

8 0-0.333 0.333-0.733 0.733-1

9 0-0.366 0.366-0.733 0.733-1

Breaks Selection

CA/TA (ha) PLAND (%) NP LPI COHESION

Habitat Type Habitat Type Habitat Type Habitat Type Habitat Type

1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

1 23275.08 26919.72 2177.1 44.44 51.40 4.16 261 201 72 29.39 32.26 0.42 99.69 99.82 96.43

2 28629.81 21562.99 2177.1 54.67 41.18 4.16 192 232 72 38.27 22.65 0.42 99.79 99.7 96.43

3 33379.29 16815.51 2177.1 63.73 32.11 4.16 158 247 72 57.24 16.05 0.42 99.91 99.56 96.43

4 23275.08 28163.34 933.48 44.44 53.77 1.78 261 197 67 29.39 34.49 0.17 99.69 99.82 94.18

5 28629.81 22808.61 933.48 54.67 43.55 1.78 192 229 67 38.27 24.78 0.17 99.79 99.70 94.18

6 33379.29 18059.13 933.48 63.73 34.48 1.78 158 243 67 57.24 17.94 0.17 99.91 99.57 94.18

7 23275.08 28752.21 344.61 44.44 54.90 0.66 261 196 28 29.39 35.55 0.1 99.69 99.82 93.07

8 28629.81 23397.48 344.61 54.67 44.67 0.66 192 228 28 38.27 25.76 0.1 99.79 99.70 93.07

9 33379.29 18648 344.61 63.73 35.61 0.66 158 242 28 57.24 18.74 0.1 99.91 99.56 93.07

Table 5. Class metric results



classification accuracy. Composition metrics such as CA/TA, NP and PLAND revealed heterogeneous structure of the landscape especially the number of patches. NP ranged from 400550 patches depending on the break selections. For example, NP for first break value is 534 with 261 patches belonging to very unsuitable, 201 for unsuitable and 72 for potentially suitable habitats. The NP for potentially suitable habitat patches decreases with decreasing pixel values from 0.733 to 1 for which NP is only 28 (Table 5). Similarly, CA/TA and PLAND indicated low amount of land occupied by potentially suitable habitat patches (PLAND = 0.664.16 %) as compared to

4060 % of land occupied by the other two habitat types respectively. On the other hand, configuration metrics such as LPI and COHESION produced expected results. LPI for potentially suitable habitat ranged from 0.10.42 % for the nine break values indicating even the largest patch occupies only 2,177.10ha of the total 52,371.90ha landscape (for first break value) (Fig. 4).

The composition metrics showed that about 1860 % of the study area is composed of unsuitable habitats for the birds that included floodplain areas near major rivers and streams, pine plantations, areas around small towns and mixed forests. The metrics further indicated

Figure 5. No perturbation.

Figure 4. 10% perturbation below nominal value.



only about 0.664.16 % of the study area is composed of potentially suitable habitat and included areas located away from urban areas and major roads. NP and COHESION exhibited severe fragmentation of potentially suitable patches in the west, central and northeast portion of study area (Fig. 7). This suggests that the isolation and size of smaller fragments might be the cause of decline of clans from those patches, because a territorial species with restrictive habitat requirements and limited gap-crossing ability will likely be sensitive to isolation effects (Conner & Rudolph 1991; Dale et al. 1994; With & Christ 1995; Pearson et al. 1996).

The connectivity metrics (COHESION) provided vital information about the structure of landscape, i.e., the patches of potentially suitable habitat patches are more physically disconnected than the other two unsuitable habitat patches. Thus we were able to show composition, configuration and connectedness of the three habitat patches that formed a heterogeneous habitat across the study area.

CONCLUSION

We presented a method of quantifying composition and configuration of possible habitats for the Red-cockaded Woodpecker using GIS, remote sensing and FRAGSTATS. We did not include other variables which may impact habitat quality and HSI such as tree age, tree diameter and tree species because these are difficult to assess from a Landsat image and measure within private

lands. Hyperspectral images can provide these variables as reported in a recent study by Santos et al. (2010). Data collected from inside the private properties would aid in validation and increase of HSI. Thus our methods could be coupled with variables obtained from hyperspectral images for better understanding of the current potential suitable habitat available in the study region. Using sensitivity analysis we were able to show that only few areas contain adequate amount of pine trees that could sustain a group of woodpeckers. However, to assess the full quality of the habitat we would require inclusion of other variables as noted above and as indicated by results from sensitivity analysis.

The results revealed a highly fragmented nature of available habitats in public and private rural lands especially near the towns of Kountze, Silsbee, and Lumberton. Most of the potentially suitable habitats were found well away from the towns, especially on the west side of the study area near Highways 326 and 421, and private lands in between them (Fig. 7). FRAGSTAT analysis revealed 344ha to 2,177ha of available potentially suitable habitat; visual inspection of the habitat suitability map shows that these habitats are highly fragmented near the towns and on private rural lands.

We assume that Red-cockaded Woodpeckers are absent near towns due to small fragments of possible habitats and lack of foraging habitat, traffic activity and patch isolation. Of 2872 suitable habitat patches, some could be more than 50 ha and are located on public and private rural lands (Fig. 7). These areas could hold some

Figure 6. 10% perturbation above nominal value.



clans of Red-cockaded Woodpeckers because of the presence of massive pockets of pure pine stands. Previous studies determined that areas larger than 50ha are able to sustain a group of Red-cockaded Woodpeckers given that they contain old, red heart fungus infected cavity trees with little or no mid-story. Landowners who have a Red-cockaded Woodpecker group or groups can do much to enhance survival regardless of the size of their property by controlling mid-stories and building artificial cavities. USFWS assists landowners to manage habitat and even provides incentives and grants to promote Red-cockaded Woodpecker conservation.

This study is confined to determining the quantity of potentially suitable Red-cockaded Woodpecker habitat in the current landscape near and around the Big Thicket National Preserve. We conclude that there are very few large patches (the largest one is 47.47ha) that can sustain a clan or populations of Red-cockaded Woodpeckers under ideal habitat conditions. Most of the patches are located within a distance of one mile

and they may be targeted for restoration and expansion efforts. Other factors such as encroaching mid-story and suppressed fire should also be assessed. To further assess the quality of these patches a thorough study of mid-story vegetation, pine tree age, diameter and species is strongly recommended in the potential suitable habitat patches. In addition, our study reveals that a woodpecker census in the region might be fruitful and that with proper management practices to preserve red-cockaded-woodpecker habitat both in private and federal lands, existing populations (if any) might be conserved and new populations might be established there.

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Threatened Taxa

The conservation status of the Fishing Cat Prionailurus viverrinus Bennett, 1833 (Carnivora: Felidae) In Koshi Tappu Wildlife Reserve, Nepal

Iain Rothie Taylor 1, Hem Sagar Baral 2, Prava Pandey 3 & Prativa Kaspal 4

1 Institute of Land, Water and Society, Charles Sturt University, PO Box 789, Albury NSW 2640, Australia2 School of Environmental Sciences, Charles Sturt University, PO Box 789, Albury NSW 2640, Australia2 Present address: Zoological Society of London (ZSL), Nepal Office, PO Box 5867, Kathmandu, Nepal 2,3,4 Himalayan Nature, PO Box 10918, Lazimpat, Kathmandu, Nepal3 Present address: Department of Environmental Science, Amrit Campus, Tribhuvan University, PO Box 102, Kathmandu, Nepal1 [email protected] (corresponding author), 2 [email protected], 3 [email protected], 4 [email protected]

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nJournal of Threatened Taxa | www.threatenedtaxa.org | 26 January 2016 | 8(1): 83238332

DOI: http://dx.doi.org/10.11609/jott.2034.8.1.8323-8332 | ZooBank: urn:lsid:zoobank.org:pub:8BFB7F65-0508-47CD-87A6-6F7B2953C5BC

Editor: Jim Sanderson, Small Wild Cat Conservation Foundation, Hartford, USA. Date of publication: 26 January 2016 (online & print)

Manuscript details: Ms # 2034 | Received 04 June 2015 | Final received 10 August 2015 | Finally accepted 05 January 2016

Citation: Taylor, I.R., H.S. Baral, P. Pandey & P. Kaspal (2016). The conservation status of the Fishing Cat Prionailurus viverrinus Bennett, 1833 (Carnivora: Felidae) In Koshi Tappu Wildlife Reserve, Nepal. Journal of Threatened Taxa 8(1): 83238332; http://dx.doi.org/10.11609/jott.2034.8.1.8323-8332

Copyright: Taylor et al. 2016. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use of this article in any medium, reproduction and distribution by providing adequate credit to the authors and the source of publication.

Funding: This project was funded primarily by the Taronga Foundation, Australia, Charles Sturt University, Australia and Chicago Zoological Society, USA.

Conflict of Interest: The authors declare no competing interests.

Author Details: See end of this article.

Author Contribution: All authors participated in field work. Prava Pandey and Prativa Kaspal were responsible for all aspects of the initial year of study. Iain Taylor and Hem Baral were responsible for project planning and management. Iain Taylor was responsible for the analysis of data and writing the paper.

Acknowledgements: This project is funded by the Taronga Foundation, Charles Sturt University and Chicago Zoological Society. We would like to thank Rebecca Spindler, Monique Van Sluys and Jo Wiszniewski of Taronga for their tremendous support and encouragement. Monique Van Sluys provided valuable and constructive comments on the draft. From 2012 this was a joint project of Charles Sturt University, Australia and Himalayan Nature, Nepal. We thank the many staff from both organisations who helped in various ways. Ashok Kumar Ram and Ganga Ram Singh of the Department of National Parks and Wildlife Conservation, Nepal provided assistance at Koshi Tappu. The Department also provided research permits. Camera traps were provided in the initial study period by Friends of Nature, IdeaWild and Dr. Jim Sanderson. We also thank Suman Acharya, Neerej Rai, Sunita Phuyal, Shambhu Ghimire and Yadav Ghimirey for their assistance in the field and Simon Macdonald of the Spatial Data Analysis Unit of Charles Sturt University for preparing the maps.

Abstract: The status of the Fishing Cat Prionailurus viverrinus in Koshi Tappu Wildlife Reserve, Nepal was assessed by camera trapping and pugmark searches from 2011 to 2014. The reserve is a highly dynamic and unstable snow-fed braided river system with many anabranches and islands. Evidence of Fishing Cats was found throughout most of the reserve. They were probably more abundant on the eastern side, among the islands of the main river channel, and in the adjacent buffer zone where there was a chain of fishponds and marsh areas fed by seepage from the main river channel. Evidence of Fishing Cats was found up to 6km north of the reserve on the Koshi River but not beyond this. The population is probably small and may be isolated but given the endangered status of the species, is significant. The main likely threats identified are wetland and riparian habitat deterioration caused by over exploitation and illegal grazing by villagers, overfishing of wetlands and rivers within the reserve, and direct persecution arising from perceived conflicts with fish farming and poultry husbandry. Required conservation actions are discussed.

Keywords: Conservation strategy, Fishing Cat, Koshi Tappu Wildlife Reserve, survey, threats.


Status of Fishing Cat In Koshi Tappu Wildlife Reserve, Nepal Taylor et al.

INTRODUCTION

The wetland dependent endangered Fishing Cat Prionailurus viverrinus (Bennett, 1833), once widespread throughout Southeast and southern Asia, now has a discontinuous distribution in Java, Thailand, Cambodia, Sri Lanka, India, Pakistan, Bangladesh and Nepal (Pocock 1939; Cutter & Cutter 2010; Duckworth et al. 2010; Mukherjee et al. 2010, 2012; Royan 2010). Available evidence suggests a recent widespread and continuing decrease in range and abundance, with loss of wetland area and quality, overfishing and direct human persecution most often cited as causes (Nowell & Jackson 1996; Mukherjee et al. 2010, 2012).

In India the species distribution includes the eastern states of West Bengal, Assam, Odisha, Andhra Pradesh and the northern Terai wetlands bordering Nepal (Sunquist & Sunquist 2002; Mukherjee et al. 2012; Sadhu & Reddy 2013; Janardhanan et al. 2014). Information is absent for most other areas but recent research failed to find any evidence of the species in coastal Kerala in southwestern India where it had been believed to occur (Janardhanan et al. 2014). Within Nepal, although the Fishing Cat is known to occur within all the protected areas of the Terai (Dahal & Dahal 2011; Jnawali et al. 2011; Mishra 2013), there have been no systematic surveys to assess abundance. There is no quantitative information on population trends or threatening processes in either India or Nepal.

The objective of this study was to assess the current status of the species in Koshi Tappu Wildlife Reserve, Nepal and by conducting an intensive standardised sampling programme, an attempt to establish a baseline against which future changes may be assessed. The species habitat was examined to determine likely threatening processes or factors that may determine future population viability. In addition, information was gathered on local villagers knowledge and perceptions of Fishing Cats, particularly those aspects that might relate to the species population viability.

STUDY AREA

Koshi Tappu Wildlife Reserve, established in 1976, extends between 8605587005E & 2603426045N on the flood plain of the Koshi River in the Terai of southeast Nepal and consists of a core area of 175km with an additional buffer zone of 173km. Between 1958 and 1964, a barrage was constructed across the Koshi River at the Indian/ Nepal border 5km south of the reserve and

embankments were constructed on both sides upstream of the barrage to contain and channel floodwater through the barrage. The current core area of the reserve, about 17km long and 9km wide now lies within these embankments. The rise in elevation between the most southerly and northerly edges of the reserve is only 20m. The entire area is a highly active snow-fed braided river system, comprising mostly river and stream courses, sandbanks, grasslands, swamps, and some areas of forest, representing newly colonising woodland and eroding old established forest. In the 1960s the main channel was on the western side of the reserve but this shifted and now lies at the extreme eastern edge. This shift has been associated with profound habitat changes across most of the reserve; between 1976 and 2010 the total area comprising wetland habitats declined by 17% whereas grassland increased by 45%. In 2010, 10% of the reserve core area was rivers and streams and only 6% swamps and marshland, whereas 56% was grassland and the remainder mainly sandbanks and forest (Chettri et al. 2013; Fig. 1).

Prior to its establishment as a reserve, Koshi Tappu was used extensively by local villagers for livestock grazing, fishing and collection of grasses and firewood. This use has continued and has resulted in severe habitat degradation including a loss of riparian vegetation. Although there has been no objective assessment of fish stocks local fishermen are clear from their catch in relation to effort that stocks have declined considerably (CSUWN 2009; DNPWC 2009).

At the time of the present study the eastern side of the reserve was least affected by overgrazing. Immediately to the west of the main river channel a series of islands were in an essentially natural condition with intact vegetation of long grasslands, swampland and forest offering cover for Fishing Cats. To the west of this, there was a zone of braided river channels most of which had either lightly or non-vegetated sand banks and islands among them offering little cover. West of this there was a mixed habitat of short and long grassland and open woodland. Much of the western side of the reserve was easily accessible to villages and overgrazing by domestic livestock was intense.

The buffer zone includes areas of the river course to the north and south of the reserve core area and areas beyond the embankments on the east and west banks sides, which are now mostly settled and cultivated. On the east side many areas of wetland that resulted from seepage through the embankment during the monsoon floods have progressively been replaced by fishponds since the 1990s (CSUWN 2009). There is a



semi-continuous chain of fishponds along this edge from about the level of Kushaha (the reserve headquarters) northwards. The ponds are mostly small (about 1,0002,000 m) and have been dug into the substrate, with earth banks and no fencing. Some are no longer in use and are being colonised by wetland vegetation, and there remain many small areas of swampland among the ponds that support populations of small fish, amphibians, molluscs and crustaceans, but they are intensively harvested by villagers for all of these species, regardless of size. On the western side of the reserve there are only minor anabranches of the Koshi River and small tributaries such as the Trijuga River, which are insufficient to maintain fishponds or significant areas of swampland. Potential habitat for Fishing Cats is more restricted.

In 2008 an exceptionally large flood broke through the embankment in the south eastern sector of the reserve, depositing large amounts of sand over wetlands, fish ponds and villages and farmland about 1 km south of Kushaha (Khatri et al. 2013). Consequently, the habitat for Fishing Cat in this area has been reduced.

During the 1950s, prior to the establishment of the barrage, the area around Koshi Tappu retained

original forest and grassland supporting populations of large predators such as Tiger Panthera tigris and Leopard P. pardus. However, following a programme of resettlement of people from the mid hill areas to the north starting in the 1950s, these large carnivores were exterminated. The exact dates are unrecorded (Chhetri & Pal 2010).

METHODS

Camera trappingDuring 2011 an initial study was conducted to

gain general information on the species distribution along the eastern side of the reserve, within the buffer zone (Image 1). Camera traps were set up in wetland habitats between 19 April 2011 and 19 June 2011 in the Prakashpur, Madhuban, Kushaha and Jabdi areas, covering all of the eastern side of the reserve from north to south. Six cameras were used over 1012 nights in each area.

From February to December 2013 an intensive programme of camera trapping was undertaken at 12 wetland sites in the eastern and western sectors of the

Figure 1. Map of Koshi Tappu Wildlife Reserve and buffer zone showing habitats and sampling sites for Fishing Cat surveys. Red spots show camera trapping sample sites and green spots show pugmark sample sites.

Prakashpur

Madhuban



buffer zone. On the eastern side ten sample sites were spaced approximately evenly throughout its length from Kushaha northwards (Fig. 1). Wetland areas were more restricted on the western side and cameras were sited at the two largest wetland areas. In addition, cameras were placed at the Koshi Tappu Bird Observatory 12km north of the reserve; an area that retained undisturbed scrub vegetation within 300m of wetland habitat and potentially a refuge for Fishing Cats. At each site cameras were placed 50100 m apart along the edges of wetland habitats and the trails connecting them. The number of cameras deployed at each site ranged from five to 13, depending on the extent of potentially suitable habitat. The grid references of each site, to enable precise future

comparisons, and their habitat characteristics are given in Table 1.

Searches were also made for evidence of Fishing Cats using pugmarks. All of the camera-trapping sample areas were searched for pugmarks on areas of soft substrate around wetlands or fish ponds on the days when the cameras were in operation. Within the reserve during February 2014, searches were made on the islands on the western bank of the main branch of the Koshi River. At approximately 1km intervals southwards from the northern edge of the reserve, sample stretches of 100m of extensive exposed soft mud on the riverbank were searched. In the central area of the reserve a similar procedure was used on one of the main anabranches wherever access was possible from a track. Pugmarks were also used to search for evidence of the presence of the cats northwards from the reserve boundary. On the eastern bank of the main river, for a total of 12km northwards, at 2km intervals, sample lengths of 300m of soft substrate were searched. A sample area immediately north of the Koshi barrage, to the south of the reserve, and another area 2km south of Kushaha in the buffer zone were also searched.

Diurnal activity patternsData on activity patterns were taken from the

programme of camera trapping described above, but to increase sample size, camera trapping was extended by a further 330 camera trap nights at 46 stations during November and December of 2013. This gave a total effort of 808 camera trap nights. As all areas were

Table 1. Location and habitat characteristics of camera trap sampling sites

Site Grid reference of centre point Main wetland type Habitat

A 26041.4N & 87004.6E Active fish pondsFarmland with active fish ponds, patches of scrub and marsh, clumps of bamboo and banana, scattered trees

B 26041.4N & 87004.8E Active fish pond & remnant marshlandFarmland with active and abandoned and overgrown fishponds, clumps of bamboo and banana, scattered trees.

C 26041.2N & 87004.5E Abandoned fish ponds, & marshland, Active and overgrown abandoned fish ponds, marshland, clumps of bamboo and banana and trees

D 26041.1N & 87003.4E Active fish ponds Active and overgrown abandoned fish ponds, clumps of bamboo and banana and trees

E 26040.4N & 87003.5E Active & abandoned fish pondsActive and overgrown abandoned fish ponds, clumps of bamboo and banana and trees

F 26040.2N & 87004.2E Active and abandoned fish pondsActive and overgrown abandoned fish ponds, clumps of bamboo and banana, scrub and trees

G 26039.3N & 87003.4E Abandoned fish ponds & marshRiverine forest with adjacent abandoned fishponds and marshland. No cultivated land

H 26039.1N & 87003.3E Active fish ponds Farmland with active fish ponds and a few scattered trees

I 26039.1N & 87003.4E Swampland and abandoned fish ponds Riverine forest with abandoned fish ponds and swampland

J 26037.3N & 87001.7E Koshi River Scrubland with scattered trees and extensive areas of bare sand

K 26038.4N & 86056.7E Trijuga River Open grazed land with scattered bushes, sandbanks and river

L 26043.5N & 87000.2E Overgrown oxbow lake Open grazed land with oxbow lake overgrown with Ipomea carnea

Image 1. Fishing Cat Prionailurus viverrinus captured by camera trap in the buffer zone of Koshi Tappu Wildlife Reserve, Nepal - 2011

Prava Pandey/Prativa Kaspal/Himalayan Nature



utilised by the local human population there was a problem of camera theft, which required cameras to be set up each day one hour before sunset and retrieved one hour after sunrise.

Villagers perceptions of Fishing CatsTo assess local knowledge and perceptions of the

species a sample of 208 adult villagers living within the buffer zones were interviewed in 2011. They were asked if they had seen Fishing Cats, where and when, what the species habitat and diet were, and also what their attitudes were towards conserving the species. Attitudes towards Fishing Cats and fishpond production were also explored by interviewing the owners or managers of a sample of 55 fish farms.

RESULTS

Distribution of Fishing CatsCamera trapping: The presence of Fishing Cats was

confirmed by camera trapping along all of the eastern buffer zone, from Kushaha northwards, and across the western buffer zone (Fig. 2). Variations in the intricate pelage patterns of Fishing Cats were used to identify individuals from the photographs where possible, but most of the images obtained were of cats moving quickly and their patterns were not adequately clear for identification. In the 2011 survey, nine separate Fishing Cats were recognised, three in the Prakashpur area, three in Madhuban, one in Kushaha and none in Jadbi. In the latter case, no images of Fishing Cats were captured.

In the intensive study of 2013/14, images of Fishing Cats were captured at nine (75%) of the 12 sites sampled (Table 2). In the majority of cases (80.4%) where Fishing Cats were photographed at a camera station only a single image was obtained per night. In three cases (5.9%) two images were obtained of cats travelling in opposite directions within 10 minutes. Although it was not know

Figure 2. Map of Koshi Tappu Wildlife Reserve and Buffer Zone showing positive sites at which evidence of Fishing Cats were found. Red spots show sites for camera trapping evidence and green spots show pugmark evidence.

Table 2. Camera trapping effort at each sample site and numbers of independent Fishing Cat images obtained, 2013.

Site Month N CamerasN

NightsN Camera/

NightsFishing Cat

imagesImages/10

camera nightsPug marks

found

A November 8 5 40 2 0.5 Yes

B November 13 10 100 1 0.1 Yes

C March 5 5 25 2 0.8 Yes

D March 7 5 35 2 0.57 Yes

E March 5 5 24 0 0 Yes

F December 8 5 36 7 1.94 Yes

G February 8 6 41 8 1.95 Yes

H December 13 4 52 11 2.12 Yes

I February 9 6 45 0 0 Yes

J March 5 5 25 0 0 No

K March 5 5 25 2 0.8 No

L March 6 5 30 13 4.33 Yes



for certain that these represented the same individual it was assumed they did and these cases recorded as only a single event. The remainder of cases (13.7%) involved apparently independent events, with more than a single individual. The number of images captured ranged from zero to 18 at different sites but there was no significant correlation between the number of images captured per 10 camera trap nights and the total number of trap nights at each site (r = -0.14, P = 0.66). Thus the number of images obtained was not related to trapping effort. In total, 10 different cats were identified but most images were too blurred for individual identification so it is not known how this number relates to the true number of individuals. The cameras captured images of all other mammals that used the tracks. Other predatory species this included the Jungle Cat Felis chaus, Small Indian Civet Viverricula indica, Jackal Canis aureus, and the Indian Fox Vulpes bengalensis (Table 3).

Pug marks: Fishing Cat pug marks were found around all camera trapping sites where photographs were obtained but were also found at one site where no photographs were obtained. At the remaining two sites where no photographs of the cats were obtained there were also no pugmarks (Table 2). Pugmarks were found at all of the sample sites within the reserve (Fig 2). On the eastern side of the reserve along the western bank of the main river course, the density of prints at all sites was exceptionally high suggesting frequent use, but elsewhere only a few prints were found in each sample suggesting much lower use. Pugmarks were found north of the reserve on the main river bank at sites 2, 4 and 6 km beyond the reserve boundary but not at sites farther north. In all cases they were single tracks with a much lower density of prints than farther south within the reserve. Pugmarks were also found at the sample site just north of the Koshi barrage, but none were found at the site 2km south of Kushaha.

Diurnal activity patternsCameras could not be left in position during daylight

hours because of the threat of theft so the information obtained relates to the period from one hour before sunset to one hour after sunrise. Only two images (4.2%

of the total) were obtained in the short period of low light of dawn immediately after sunrise and none in the hour before sunset. At these times on all cameras images of villagers were captured on all the trails as they returned home or started their daily activity. In the intervening period of darkness, there was little evidence of any pattern: the cats seemed to be equally active throughout the entire night with perhaps slightly lower activity immediately before sunrise (Fig. 3).

Villagers knowledge of Fishing CatsSeventy eight percent (78%) of the 208 villagers

interviewed stated they had seen Fishing Cats, and 66% stated they had seen them around their farms, either at fishponds or at poultry cages. Of those that reported seeing the cats, 83% stated they had seen them in the first light of morning and 17% during darkness. No one reported seeing the cats during daylight hours. All of those interviewed stated that Fishing Cats feed on fish, crustaceans and snails and all of them also stated that their habitat was wetlands. Half (50%) of those interviewed believed that the Fishing Cat population in the area was declining, 16.5% believed the population to be increasing, 16.5% suggested no recent change and the remainder had no opinion. Sixty-seven percent (67%) were in favour of conserving the species and 33% wanted them killed because of perceived problems as predators of fish in ponds or of poultry. Only 8% knew that the Fishing Cat was an endangered species. Some suggested that Fishing Cats were already persecuted in retaliation for taking chickens and fish and that attempts were made by some to kill them by either electrocution or poisoning around fish ponds. Two authenticated cases of this were established during the course of the 2013/14 study period, one of which involved a cat

Table 3. The total numbers of all other predatory mammals photographed in camera traps

Fishing Cat Jungle Cat Small Indian Civet Jackal Indian Fox

48 9 7 23 1

Figure 3. Activity pattern of Fishing Cats at Koshi Tappu Wildlife Reserve from one hour before sunset to one hour after sunrise. Data from camera trapping in 2013.

Hours before and after sunset

-2-0 0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16

Perc

enta

ge o

f im

ages

0

5

10

15

20

25

30

N = 96

sunr

ise

suns

et



intercepted while trying to raid a poultry house.

Aquaculture methods and interactions of Fishing Cats with fish farms

All fishpond owners reared a mixture of species of Carp (Family Cyprinidae), including Rohu Labeo spp., Naini Cirrhinus mrigala, Bighead Carp (Thul Tauke) Aristichthys nobilis, Commom Carp Cyprinus carpio, Grass Carp (Ghase Machha) Ctenopharyngodon idellus and Silver Carp (Chade Machha) Hypophthalmichthys molitrix. The ponds were stocked with fry each year, usually from April to May but sometimes also in March depending on the amount of water in the ponds and temperatures. Fry were purchased either from a local hatchery or from dealers from India. The price of local fry in 2014 was 300500 Nepali rupees per kilo for locally produced fry and 500 Nepali rupees for Indian fry. Fish were fed mainly with pellets made by the owners from local produce, mostly cornflour, ground cereal husks and mustard oil. The growt

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