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Development of Multispecies, Long-Term Monitoring Programs for ☆ Resource Management

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Development of Multispecies, Long-Term Monitoring Programs for ☆ Resource Management Rangeland Ecology & Management 72 (2019) 168–181 Contents lists available at ScienceDirect Rangeland Ecology & Management journal homepage: http://www.elsevier.com/locate/rama Development of Multispecies, Long-Term Monitoring Programs for ☆ Resource Management Jeremy A. Baumgardt a,⁎, Michael L. Morrison b, Leonard A. Brennan c, Brian L. Pierce a, Tyler A. Campbell d a Texas A&M Natural Resources Institute, College Station, TX 77843, USA b Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX 77843, USA c Caesar Kleberg Wildlife Research Institute, Texas A&M University, Kingsville, TX 78363, USA d East Foundation, San Antonio, TX 78216, USA article info abstract Article history: There is growing interest among resource managers in implementing long-term wildlife monitoring. The process Received 9 June 2017 to develop such a program may seem daunting, however, because it requires determining the species, metrics, Received in revised form 11 July 2018 sampling methods, experimental design, and level of effort necessary to achieve the desired power for detecting Accepted 14 July 2018 meaningful changes. Failure to give these decisions proper attention often leads to suboptimal information for decisions and planning objectives. Our primary objectives were to develop alternative scenarios for a monitoring Key words: program, including power estimates and sampling effort required to detect population changes for small mam- monitoring multispecies mals on rangelands in southern Texas. Our secondary objective was to present a framework for developing power analysis customized monitoring programs for tracking wildlife populations over time. We trapped small mammals rodents using ~28 000 trap nights each year from 2014 to 2016 resulting in 13 183 captures of nine species. We estimated small mammals abundances and occupancy in each year for each species and conducted power analyses using simulations. We used these results to develop four multispecies monitoring scenarios: two with distinctly different levels of effort with abundance as the focal metric and two for monitoring occupancy. The most effort-intensive scenario re- quired trapping 40 grids for 6 consecutive nights each yr. With this effort, we predicted it would be possible to detect annual changes in abundance of ≤ 10% after 10 yr for four species and net declines in occupancy of ≤ 50% after 10 yr for five species with a power of 0.90. The least effort-intensive scenario required trapping 30 tran- sects for 4 consecutive nights each yr. We predicted this effort would allow for the detection of annual changes in occupancy rates between 35% and 55% after 10 yr for five species. Our study is an example for land managers, pro- viding general guidelines for developing rigorous, long-term monitoring programs specifictotheirobjectives. © 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved. Introduction monitoring program may seem daunting. Proper planning and implementing of a monitoring program includes identifying an appro- There is growing interest among resource managers in priate species or taxa (Carignan and Villand, 2002), selecting metrics implementing long-term wildlife monitoring programs. Data from that are sensitive to changing conditions (Williams et al., 2002), monitoring have substantial value for detecting relationships between selecting sampling methods that best maximize efficiency (Garton et management actions and animal populations (Pollock et al., 2002)and al., 2005), using an experimental design to isolate the hypothesis of in- should provide direction regarding future management decisions terest (e.g., change detection) with the most efficient probabilistic sam- (Nichols and Williams, 2006; Kendall and Moore, 2012). In addition, pling (Garton et al., 2005; Morrison et al., 2008), and employing monitoring that is not otherwise driven by a specific hypothesis can sufficient effort (sample size) to achieve the desired level of power for help researchers understand impacts of unplanned events such as detecting biologically meaningful changes (Fig. 1; Williams et al., weather (short term) and climatic patterns (long term) on wildlife pop- 2002; Field et al., 2007). Failure to give these decisions proper attention ulations (Beever and Woodward, 2011; Fancy and Bennetts, 2012; John- often leads to misallocated resources, resulting in suboptimal informa- son, 2012). However, formal processes necessary to implement a tion for decisions and planning objectives (Yuccoz et al., 2001; Legg and Nagy, 2006). In addition, incorporating statistical power analysis into program design can ensure sampling efforts are optimized such ☆ The East Foundation, San Antonio, Texas, funded this research. that more effort than is required to meet stated objectives is not used ⁎ Correspondence: Jeremy A. Baumgardt, Texas A&M Natural Resources Institute, 578 John Kimbrough Blvd, College Station, TX 77843, USA. (Lenth, 2001; Reynolds et al., 2011). Considering the complexities of E-mail address: [email protected] (J.A. Baumgardt). the relationship among sampling objectives, experimental designs, https://doi.org/10.1016/j.rama.2018.07.010 1550-7424/© 2018 The Society for Range Management. Published by Elsevier Inc. All rights reserved. J.A. Baumgardt et al. / Rangeland Ecology & Management 72 (2019) 168–181 169 Figure 1. Framework for developing a customized, multispecies monitoring program. If prior data that were collected using methods proposed for the monitoring program already exist, it may not be necessary to collect additional data. However, preliminary data should also be specific to and representative of the spatial location of the proposed monitoring program. power, and effort, it is also cost effective to evaluate and compare alter- San Antonio Viejo Ranch to ~66 cm at the El Sauz Ranch (NOAA, 2016). native monitoring scenarios. Annual weather patterns were highly variable in southern Texas, with Our primary objectives were to 1) develop and evaluate alternative rainfall totals over the 12-mo periods ending 30 April 2014, 2015, and monitoring scenarios focused on detecting spatial-temporal changes 2016 on the San Antonio Viejo Ranch of 47.3 cm, 59.8 cm, and 49.1 in species or assemblage of small mammals within a rangeland system, cm, respectively. Rainfall totals for the same periods for the El Sauz 2) estimate power and sensitivity for our proposed monitoring scenar- Ranch were 52.6 cm, 94.9 cm, and 72.2 cm, respectively. ios, and 3) characterize appropriate sampling effort required and The San Antonio Viejo Ranch was a matrix of woodland (73%) and resulting level of sensitivity for each scenario. Our secondary objective shrubland (18%), with approximately half of the ranch in the Coastal was to present a framework that can be replicated elsewhere for devel- Sand Plain Ecoregion and half in Texas-Tamaulipan Thornscrub (Dia- oping customized monitoring programs for tracking wildlife popula- mond and Fulbright, 1990; Fulbright et al., 1990; McLendon et al. tions over time. 2013b). El Sauz was located 117 km to the east of San Antonio Viejo and adjacent to the Laguna Madre along the Texas Gulf Coast. El Sauz Step 1. State Objectives was 36% woodland, 30% wetland vegetation, and 26% grassland (McLendon et al., 2013a). Sixty percent of El Sauz was in the Coastal The first step in developing a monitoring program requires identifi- Sand Plain ecoregion, with the remaining 40% split evenly between cation of details that will define the focus of the program and guide fur- the Laguna Madre Barrier Islands and Coastal Marshes ecoregion and ther decisions. This includes defining the spatial area of interest, the the Lower Rio Grande Valley ecoregion (Diamond and Fulbright, 1990; species or taxa to monitor, and the state variable(s) of the species to Fulbright et al., 1990; Forman et al., 2009). measure (Fig. 1A). Select Monitoring Taxa Define the Area Selection of an appropriate focal species or group to monitor de- The appropriate spatial area for developing a monitoring program pends on the program’s objectives (Yuccoz et al., 2001; Wiens et al., should be explicit from the program’s objectives (Olson et al., 1999). 2008). Monitoring for a specific species of concern, to detect impacts The objectives for our study specified the ranches owned by the East of management actions, or as an indicator of biodiversity or ecosystem Foundation as the area for our monitoring program. The East Founda- process would likely all result in selecting different focal species (Caro tion is composed of approximately 87 000 ha of rangeland in southern and O’Doherty, 1999). We assume the objectives have been clearly Texas that is managed as a working laboratory to support wildlife con- stated, including identification of the monitoring species, before the de- servation, private land stewardship, and other public benefits associated velopment phase that our paper attempts to address. Thus, we limit our with ecologically sound cattle ranching (Fig. 2). We collected data on coverage of concepts that should be considered when selecting a focal the 60 752-ha San Antonio Viejo (Jim Hogg and Starr Counties) and 10 species or taxa to a brief summary. When the focal species is not explic- 984 ha El Sauz (Willacy and Kenedy Counties) ranches. Annual precipi- itly identified in the objectives, a number of criteria have been identified tation averages for the period of 1981–2010 ranged from ~57 cm at the to help guide the decision, such as species that can be monitored
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