Playing “hide and seek” with Texas tortoises: value of a detector dog

Texas tortoises (Gopherus berlandieri) were once considered common and abundant throughout southern Texas with densities as high as 16 tortoises per hectare. Today, density estimates are 0.25 tortoises per hectare, which constitutes about a 98% population decline. Because of their low numbers and elusive behavior, Texas tortoises can be difficult to find. We demonstrate the value of using a detector dog as a time saving method in locating Texas tortoises. We glued VHF radio transmitters onto 9 adult tortoises and released them in a 5-ha plowed and short-grass pasture that contained mesquite (Prosopsis glandulosa) mottes, habitat conducive for Texas tortoise habitat selection. We calculated the Detectability Index (DI) as the detection rate (# tortoises found/minute) × percent tortoises from the known population found within 60 minutes. We compared DIs via telemetry, detector dog, and “cold” (no equipment or knowledge) human searches. We used the time required to find all tortoises when a searcher had knowledge of locations as the baseline. Our baseline DI was 0.79, followed by telemetry (0.13) and detector dogs (0.11), while “cold” searches was 0.02. Telemetry, detector dog, and cold searches were 6-fold, 7-fold, and nearly 40-fold slower, respectively, than having knowledge of tortoise locations. However, the combination of using detector dogs with telemetry resulted in a 50% time savings than single methods. Telemetry was useful in locating a generalized area with a tortoise but a detector dog was 2X faster in visually locating the tortoise once the area was identified. Therefore, we recommend the use of detector dogs as a time-saving method when conducting research on Texas tortoises

A Nondestructive Method to Estimate Standing Crop of Purple Threeawn and Blue Grama

We used multiple regression analysis to develop models to predict standing crop of purple threeawn (Aristida purpurea Nutt.) and blue grama (Bouteloua gracilis [H.B.K.] Griffiths) nondestructively. Data were collected for 3 yr on the Texas Tech University Native Rangeland, Lubbock, TX, USA. Independent variables included plant length and area measurements (basal area and cross-sectional area at a 7.5-cm plant height and at 50% of total plant height). One hundred randomly selected plants of each species were measured in June 2008; 50 plants of each species were measured in June 2009 and 2010. Coefficients of determination exceeded 0.91 for both species in all 3 yr of measurement. For both species and years, cross-sectional area at 7.5 cm was the most important single predictor variable. For each species, models differed among years. Our regression models were successful at predicting mid- to late-season standing crop of purple threeawn and blue grama grass and provide an effective method for nondestructive monitoring of these species. This approach should be applicable to similar morphotypes of these species./Usamos un análisis de regresión múltiple para desarrollar modelos no destructivos para predecir la producción de purple threeawn (Aristida purpurea Nutt.) y blue grama (Bouteloua gracilis [H.B.K.] Griffiths). Los datos fueron recolectados durante 3 años en el pastizal nativo de Texas Tech University, Lubbock TX, USA. Variables independientes incluyeron longitud de la planta, y mediciones de área (área basal y área de la sección transversal a 7.5 cm de la altura de la planta, área de la sección transversal al 50% del total de la longitud de la planta). Cien plantas de cada especie fueron seleccionadas aleatoriamente y medidas en junio de 2008; 50 plantas de cada especie fueron medidas en junio de 2009 y 2010. Los coeficientes de determinación excedieron 0.91 para ambas especies durante los tres años que se llevaron a cabo las mediciones. Para ambas especies y años, el área transversal a la altura de 7.5 cm fue la variable única de predicción más importante. Para cada especie,los modelos fueron diferentes entre años. Nuestros modelos de regresión fueron exitosos en la predicción de la biomasa en la etapa media a tardía de crecimiento de de los pastos purple threeawn y blue grama y proporcionan un método efectivo no destructivo para el monitoreo de estas especies. Esta metodología debería ser aplicable para morfo tipos similares de estas especies.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202

Biomass Not Linked to Perennial Grass Mortality Following Severe Wildfire in the Southern High Plains

In March 2006 the East Amarillo Complex (EAC) wildfires burned over 367 000 ha of short and mixed grass prairie of the southern High Plains, USA. We studied EAC wildfire effects on perennial grass mortality and peak standing crop on Deep Hardland and Mixedland Slopes ecological sites. Deep Hardlands were dominated by blue grama (Bouteloua gracilis H.B.K. [Griffiths]) and buffalograss (Buchloe dactyloides [Nutt.] Engelm.); common species on Mixedland Slopes were little bluestem (Schizachyrium scoparium [Michx.] Nash.) and sideoats grama (Bouteloua curtipendula [Michx.] Torr.) with scattered sand sagebrush (Artemisia filifolia Torr.) sometimes present. We hypothesized that perennial grass mortality would increase and standing crop would decrease following severe wildfire, and that these responses would be greater than documented prescribed fire effects. Frequency of perennial grass mortality was higher on both sites in burned areas than nonburned areas through three growing seasons following wildfire; however, standing crop was minimally affected. Results suggest that post-wildfire management to ameliorate wildfire effects is not necessary, and that wildfire effects in this area of the southern High Plains are similar to prescribed fire effects. The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202