Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data

Abstract

Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.

Density of a cryptic Australian small mammal: The threatened Julia Creek dunnart (Sminthopsis douglasi)

“…From a global perspective, our density estimates for S. douglasi were substantially lower than density estimates of international threatened small mammals (albeit rodents), such as the salt marsh harvest mouse (Reithrodontomys raviventris; minimum density estimate 9.13 ± 2.70 individuals ha −1 ; Freeman et al, 2022), the Key Largo woodrat (Neotoma floridana smalli; minimum density estimate 3.1 individuals ha −1 ), the cotton mouse (Peromyscus gossypinus allapaticola; minimum density 15.5 individuals ha −1 ; Humphrey, 1988) and the Kondana soft-furred rat (Millardia kondana; minimum density 2.01 individuals ha −1 ; Bajaru & Manakadan, 2020). However, Australian semi-arid and arid small mammals are known to be highly mobile and occur at low densities, likely due to spatiotemporally patchy resources and low habitat productivity, so this may in part account for the generally lower small mammal densities found here (Dickman et al, 1995).…”

Exploring patterns of female house mouse spatial organisation among outbreaking and stable populations

“…From a management perspective, our study offers valuable insights into the importance of balancing monitoring location and trapping effort (Freeman et al, 2022;Gerber & Parmenter, 2015) within grain cropping regions. In addition, the low SECR spring density estimates observed in our study and knowledge on the tendency of SECR models to, on average, underestimate density highlight the importance of refining approaches to mouse population monitoring and density estimation (Gerber & Parmenter, 2015).…”

Application of lidar to assess the habitat selection of an endangered small mammal in an estuarine wetland environment

“…Prior to widespread availability of lidar, measurements of vegetation structure were often compiled as field‐based metrics collected by hand (Jaime‐González et al., 2017; Koma et al., 2020). These measurements can be somewhat coarse or subjective and collecting the data may be costly and time‐consuming (Freeman et al., 2022; Jaime‐González et al., 2017; Vierling et al., 2008). Therefore, lidar may allow users to quantify vegetation structure in habitats at a much finer scale while covering a much broader extent (Hagar et al., 2020; Vierling et al., 2008).…”