1. Introduction
For water-dependent organisms, hydroperiod – or the amount of time a lentic waterbody holds water – plays a critical role in population and community dynamics (De Meester et al., 2005). Dispersal decisions (Tournier et al., 2017), fitness (Johnson et al., 2013; Rogers & Chalcraft, 2008), reproductive success (Ryan & Winne, 2001), survival (Acosta & Perry, 2001), and source-sink dynamics (Ruetz III et al., 2005; Werner et al., 2007) are all influenced by hydroperiod. Hydroperiod is also an important predictor of community composition (Razgour et al., 2010; Skelly, 1997; Waterkeyn et al., 2008) and diversity (Schriever et al., 2015; Schriever & Williams, 2013; Stendera et al., 2012), and thus may influence ecosystem stability. Specific components of hydroperiod, such as inundation timing or stability, influence species density and richness (Florencio et al., 2020; Kneitel, 2014) and may play a key role in determining reproductive success of aquatic organisms such as amphibians (Paton & Crouch III, 2002).
The wide-ranging effects of hydroperiod on individual organisms, populations, and ecological communities necessitate tools to enable fine-scale measurement and monitoring of the timing, frequency, and duration of hydroperiod events in temporary lentic waters. Such tools will play an important role in predicting how hydroperiods may change in response to future water use and climate scenarios – and how organisms that rely on these habitats will fare. Satellite remote sensing tools such as Synthetic Aperture Radar (Bourgeau-Chavez et al., 2005; Hong et al., 2010) and Landsat imagery (DeVries et al., 2017; Díaz-Delgado et al., 2016; Murray-Hudson et al., 2015) enable wetland hydroperiod assessment over multi-year or multi-decade periods and covering 10s to 1000s km2. The accuracy of these methods continues to improve with advances in image analytical techniques that provide information on surface water presence and area at a sub-pixel level (Halabisky et al., 2018). Unmanned aerial systems (i.e., drones) can also provide high-resolution spatial and temporal data, particularly for specific regions (e.g. Levy & Johnson, 2021). Despite these promising advances, the temporal grain of remotely sensed data remains coarse for most remotely sensed datasets. For example, Landsat captures images at a spatial resolution of 30 meters every 16 days (Irons et al., 2012; Ozesmi & Bauer, 2002). In many regions, inundation of intermittent lentic habitat may occur over hours or days. Temporal resolution on the order of 2-4 weeks may miss important fine-scale differences in pond inundation timing, particularly in regions with unpredictable spatial patterns of precipitation that drive a patchwork of inundation dates. More recently, new earth observation products continue to refine the temporal grain size of satellite data, with some providing daily or sub-weekly observations (Lefebvre et al., 2019). Spatial resolution of new commercial products offer sub-meter resolution in some cases (e.g., Planet Team 2021). Though these products do not have the historical breadth of Landsat, they offer much higher spatial and temporal resolution for more recent years. However, challenges remain. Some remotely-sensed hydroperiod data can be obscured by cloud and/or canopy cover, decreasing temporal resolution from a few days to longer periods. This may be particularly challenging if inundation timing co-occurs with periods of high cloud cover. Though commercial products are often available at no charge for researchers with particular affiliations, data in these cases is often limited in format, spatial extent, or resolution. For full access, project budgets often must accommodate product costs.
Temperature and conductivity sensors are used increasingly in both lotic and lentic systems to provide fine-scale spatial and temporal hydroperiod measurements (Anderson et al., 2015; Arismendi et al., 2017; Jaeger & Olden, 2012). Daily temperature variance is typically lower in water than in air, and comparison of daily temperature variance provides a reliable proxy for inundation state (Sowder & Steel, 2012). A rapid drop in daily temperature variance can reliably measure the precise timing of an inundation event (Anderson et al., 2015; Arismendi et al., 2017). For example, Anderson et al. (2015) tested the ability of temperature sensors to accurately predict inundation states both in natural wetlands and in controlled mesocosms. The authors deployed temperature sensors for two six-month periods in ponds over a 7140 ha area that varied in size and depth. They demonstrated that daily temperature variance reflected pond filling and drying events, with higher variance in dry ponds and in control sensors placed on the ground outside of ponds, and they determined an approximate variance threshold to predict inundation states. Arismendi et al. (2017) placed paired temperature sensors and electrical resistors in temporary streams and found that using daily temperature standard deviation more accurately predicted inundation states than mean hourly or daily temperature measurements.
Hidden Markov models (HMMs) can be used to identify shifting trends in time series data (e.g. high temperature variance associated with dry states and low temperature variance associated with wet states) while accounting for temporal autocorrelation and are useful tools for modeling climatic data (reviewed in Srikanthan & McMahon, 2001). Arismendi et al. (2017) demonstrated the ability of these algorithms to predict wetland inundation states and found that fitting 2-state HMMs to temperature standard deviations led to accurate predictions of shifts from wet to dry states in ephemeral streams.
Here, we describe methods for deployment and data analysis of an array of temperature loggers to monitor inundation state of intermittent ponds in the San Rafael Valley of Arizona, USA. These methods are applicable to a range of temporary lentic habitats, especially where logistical challenges may necessitate use of on-the-ground measurements rather than remotely sensed data. The objectives of this study were: 1) design a sturdy, low cost, and low maintenance housing unit for temperature sensor deployment in remote and rugged terrain; 2) deploy paired sensors (one within the target pond and one outside the pond) to monitor hydroperiod inundation states in temporary ponds; 3) evaluate inundation states using HMMs, comparing inundation date inference between 1-logger (pond only) and 2-logger (pond + control) experimental design; 4) compare observed and inferred inundation state recorded during in-person visits to ponds. Overall, our findings point to the utility of temperature loggers as a cost-effective, low profile tool in uncovering ecologically relevant spatiotemporal differences in intraregional inundation timing. This is particularly useful in regions with highly localized precipitation events that drive small-scale differences in spatiotemporal hydroperiod dynamics.