Optimal spatio‐temporal hybrid sampling designs for ecological monitoring

Abstract

Question: Static sampling designs for collecting spatial data efficiently are being readily utilized by ecologists, however, most ecological systems involve a multivariate spatial process that evolves dynamically over time. Efficient monitoring of such spatio‐temporal systems can be achieved by modeling the dynamic system and reducing the uncertainty associated with the effect of design choice at future observation times. However, can we combine traditional techniques with dynamic methods to find optimal dynamic sampling designs for monitoring the succession of a herbaceous community? Location: Lower Hamburg Bend Conservation Area, Missouri, USA (40°34′42″ lat. 95°45′38″ long.). Methods: The dynamic nature of the system under study is modeled in such a way that uncertainty in the measurements and temporal process can both be accounted for. Both fixed and roving monitoring locations were used in conjunction with a spatio‐temporal statistical model to efficiently determine optimal locations of roving monitors over time based on the reduction of uncertainty in predictions. Results: During the first 3 years of the study, roving monitors where held at fixed locations to allow for statistical parameter estimation from which to make predictions. Optimal monitoring locations for the remaining 2 years were selected based on the overall reduction in prediction uncertainty. Conclusions: The dynamic and adaptive vegetation monitoring scheme allowed for the efficient collection of data that will be utilized for many future ecological studies. By optimally placing an additional set of monitoring locations, we were able to utilize information about the system dynamics when informing the data collection process.

Monitoring dynamic spatio‐temporal ecological processes optimally

“…Probabalistic (i.e., designbased) surveys have been used widely in ecology, and can provide data that result in objective, unbiased estimates of abundance (Cochran 2007, Thompson 2012. Further, dynamic survey designs better allow for the observation of dynamically evolving spatio-temporal processes, and ultimately result in higher quality data (Wikle and Royle 1999, Hooten et al 2009). The situation becomes more accute for populations that are spreading in space through time.…”

“…However, when financial resources limit the effort that can be devoted to collecting data, classical design-based inference may result in estimates that are insufficiently precise for management or conservation (e.g., sea otters in Glacier Bay). However, they have been used in relatively few long-term ecological studies (Hooten et al 2009). Alternatively, optimal dynamic survey designs allow managers and scientists the ability to extract the most information out of the data they can afford to collect.…”

“…If we can predict future behavior, and the associated uncertainty, we can then choose survey locations that help reduce uncertainty in our understanding of the process (Hooten et al 2009). Population spread is an ecological process that evolves spatially through time.…”

“…We distinguish dynamic survey designs from the traditional statistical notion of adaptive sampling (sensu Thompson 1990), although the two concepts are related. However, dynamic survey designs have been applied to few long-term ecological monitoring programs (e.g., Wikle and Royle 2005, Hooten et al 2009, 2012, Evangelou and Zhu 2012. However, dynamic survey designs have been applied to few long-term ecological monitoring programs (e.g., Wikle and Royle 2005, Hooten et al 2009, 2012, Evangelou and Zhu 2012.…”

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

“…Interestingly, Hoeting et al (2000) and Wintle and Bardos (2006) obtained similar results with their simulated data refl ecting plant and mammal distributions. Both static and dynamic designs have advantages and disadvantages (MacKenzie and Royle 2005, Wikle and, but an appropriate allocation of sampling eff ort between fi xed and roving units may contribute to combining several monitoring and mapping objectives (Hooten et al 2009). First, our results revealed considerable among-species variation in this minimum sample size.…”