Cooperative Research Units
Education, Research And Technical Assistance For Managing Our Natural Resources
Home | Intranet | Digital Measures | Help

Assessment of abundance and diet of felids at Kofa National Wildlife Refuge


April 2008 - May 2010


Objective 1. Determine DBS condition and its influence on pregnancy, juvenile production, juvenile survival, and adult survival.

A. Capture and maintain 30-40 radio-collared ewes annually. DBS will be captured by aerial darting from a helicopter and fitted with a uniquely marked VHS radio-collar and a subset (up to 50%) with satellite GPS collars. Radio-collared DBS will be recaptured each following autumn for subsequent condition assessment (see below)..

B. Measure body condition and general health of captured females. Ultrasonography will be used to measure maximum rump fat thickness and thickness of the longissimus dorsi muscle to assess fat and lean muscle reserves (Cook 2000, Bender et al. 2007a). Condition of DBS will also be assesses using body condition scoring systems (Bender et al. 2007a). Additionally, blood and fecal samples will be collected to survey herd health by screening for exposure to selected disease processes and parasites (Harder and Kirkpatrick 1996). Health assessment will include Pasteurella/Mannheimia bacterial cultures, and viral antibody tests for parainfluenza 3, bovine respiratory syncital virus, epizootic hemorrhagic disease/bluetongue viruses, and other disease processes that may be identified as a concern. Disease testing will be performed by Veterinary Diagnostic Services, Albuquerque, NM, USA.

Adult females will be recaptured each following fall to re-evaluate body condition. Relationships between pregnancy status (pregnant/not pregnant), lactation status (lactating/not lactating), and lamb survival (live/die) and body condition will be modeled using logistic regression (following Bender et al. 2007a, c). Recapture and annual condition assessments allow determination of annual variation in habitat quality, prediction of individual and population productive potential, and the identification of annual nutritional deficiencies. For example, if spring-autumn ranges are nutritionally deficient, condition of lactating ewes will be significantly lower than non-lactating (Clutton-Brock et al. 1982, Verme and Ullrey 1984, Piasecke 2006, Piasecke and Bender 2008).

C. Relate condition to production, viability and survival of juveniles. �regnancy will be determined each capture by ultrasonography of radio-collared ewes or blood chemistry (i.e., presence of pregnancy-specific placental protein B; BioTracking, Moscow, ID, USA). Intensive monitoring of radio-collared ewes with lambs at heel, and/or comparisons of individual and population-level pregnancy and subsequent lactation (Bender et al. 2002), will be used to determine survival of juveniles. Pregnancy, lactation, and survival of juveniles will be modeled using maternal and mean condition levels of the population as well as environmental variables (i.e., seasonal and annual precipitation, etc) following Lomas and Bender (2007).

D. Relate condition to adult/juvenile survival and causes-of-mortality. Survival staus of each DBS will be determined approximately every other day (for VHF collars) and daily (for satellite collars) by either ground telemetry or from realtime observations of movements of satellite collars from webservers. Causes of mortality for each adult ewe will be determined (Lomas and Bender 2007, Bender et al. 2007a), and radio-collars reallocated during subsequent recaptures. Survival and cause of mortality will be related to condition of ewes and other environmental and habitat attributes (i.e., seasonal and annual precipitation, home range composition, etc.), and the specific predictive relationships between a priori condition and other variables with survival of adults modeled following Bender et al. (2007a) and Lomas and Bender (2007). This will allow determination as to whether DBS are predisposed to factors such as predation or whether such factors are additive and thus limiting the DBS population. Population survival rates will be estimated using the staggered-entry Kaplan-Meier estimator (Pollock et al. 1989) and cause-specific mortality rates determined using the approach of Heisey and Fuller (1985). Factors related to lamb survival including maternal condition will be modeled identically.

E. Compare condition of lactating and dry ewes. Body condition of lactating and dry ewes will be compared. Under conditions of no nutritional limitations, the energetic costs of lactation will not preclude lactating females from achieving comparable fat and other energy reserves as dry females (Clutton-Brock et al. 1982, Cook et al. 2004, Piasecke 2006, Piasecke and Bender 2008). As nutritional stress increases, the differences in accretion of reserves between lactating and dry females increases (Piasecke and Bender 2008), indicating progressively poorer habitat quality. Given sufficient annual variation, it is possible to model proximity of a population to ecological carrying capacity based upon these differences in fat accrual of lactating and dry females (Piasecke and Bender 2008).

Objective 2: Determine DBS habitat use patterns and identify key habitat features.

A. Locate DBS ~1-2 times weekly. � DBS will be located a minimum of twice weekly for assessment of survival, and 1-2 times weekly for observation of lambs-at-heel and habitat relationships, with vegetation type at location, behavior, and location of adjacent large herbivores, if present, recorded for each location. This will provide information on DBS distribution relative to human/natural factors, and on DBS habitat use. All telemetry locations will be from the ground, with a spotting scope used for focal animal observations.

B. Determine distribution and habitat use patterns.�DBS locations will be plotted on GIS coverages to allow spatial analysis of DBS movements, 2nd and 3rd order habitat use (Johnson 1980), and distribution with respect to habitat features and other herbivores. DBS home ranges and core use areas will be identified using an adaptive kernel estimator (Kie et al. 1996). Habitat correlates associated with DBS distribution patterns will also be derived from maximum entropy modeling using presence data in program MAXENT (Phillips et al. 2006). This will provide data on relative use areas of DBS, as well as habitat attributes associated with use areas.

C. Map all land management activities. �All existing and proposed land management activities and stand data will be mapped. DBS distribution from Objective I.2B will be spatially analyzed relative to past/current management practices using multi-response permutation procedures (MRPP; Slauson 1991) and modeled using maximum entropy presence modeling (Phillips et al. 2006). For example, do DBS preferentially use prescribed or wildfire burns for foraging?

D. Model fat accretion in DBS. Fat levels of individual DBS will be modeled as a function of attributes of their annual and seasonal home ranges, including vegetation-type associations; distance relationships to water, roads, and other habitat attributes; seasonal and annual precipitation patterns; and landscape greenness as determined by either AVHRR or Landsat tasseled cap remote sensing indices (Smallidge 2005, Davis 2005) using multiple regression following Bender et al. (2007c). This analysis will identify which habitat attributes are most positively or negatively related to accretion of body fat of DBS, and thus which habitat attributes most fundamentally influence productivity of the KOFA DBS population.

Objective 3. Determine kill rates and predation impacts of lions on KOFA.

A. Cause-specific predation rates. �Predation rates on DBS will be determined from cause-specific analysis of DBS survival/mortality rates (see Objective I.1D above). This will determine what proportion of the DBS population is killed annually by large predators, and, combined with data on factors affecting survival of DBS (see Objective I.1D above), determine whether predation is a significant factor in DBS survival on KOFA. For example, if modeled relationships between survival of adult sheep and body condition show a strong sigmoidal shape, then 1 or more thresholds exist below which survival probability is extremely low and hence mortality to any factor is strongly predisposed. Alternatively, if logistis models between survival and condition are more linear, then no strong degree of predisposition likely exists in individual DBS as condition declines and mortality from any factor is likely to unrelated to condition and hence resource capture.

Objective 4. Determine habitat correlates of kill sites on KOFA and develop management strategies to decrease vulnerability of DBS.

A. Kill location analysis. �Predator kills seldom occur randomly on the landscape. For example, Rosas-Rosas (2006) found jaguar and lion kills of cattle occurred closer to water and in more thickly vegetated cover types than expected by chance. Analysis of kill locations allows identification of vegetation and other habitat structural characteristics that may increase vulnerability of DBS to predation. Identification of such features, if they occur, allows habitat management practices to be used to mitigate predation. Further, it allows identification of key areas for habitat management practices. Presence modeling (for predator kills only) and binary response logistic regression (outcome = predator kill or other mortality) will be used to derive habitat correlates associated with successful predation events (MacKenzie et al. 2006, Rosas-Rosas 2006, Baldwin 2007; L. Bender, unpublished data). Predictor variables will include vegetation type and structure, topography, distance to water, landscape roughness coefficient derived from DEM spatial data, and other landscape attributes identified as important in habitat selection of DBS (see above).

B. Map habitat attributes associated with DBS vulnerability. �Habitat attributes found to be associated with successful predation events will be mapped to identify areas of high vulnerability of DBS. This information can be used to identify and prioritize treatment areas to decrease extant predation levels if necessary.

C. Develop management strategies to decrease DBS vulnerability. �Management strategies that decrease vulnerability of DBS to predation will be developed based upon habitat attributes determined to affect vulnerability. For example, vulnerability to predation by jaguars is primarily associated with dense riparian vegetation in northern Sonora, Mexico (Rosas-Rosas 2006). Management strategies that could decrease vulnerability of herbivores to jaguar predation include development of water sites in uplands. Similar habitat management strategies can be developed for KOFA once attributes associated with successful predation events are identified.


Current Staff

Federal Staff: 102

Masters Students: 247

Phd Students: 163

Post Docs: 55

University Staff: 266

5 Year Summary

Students graduated: 722

Scientific Publications: 1960

Presentations: 4355



Funding Agencies

  • Science Support Partnership
  • SPP State Partnership Program


Cooperative Research Units Program Headquarters Cooperators