As sea levels rise and temperatures increase, vegetation communities in tropical and sub-tropical coastal areas will be stressed; some will migrate northward and inland. The transition from coastal marshes and scrub–shrubs to woody mangroves is a fundamental change to coastal community structure and species composition. However, this transition will likely be episodic, complicating monitoring efforts, as mangrove advances are countered by dieback from increasingly impactful storms. Coastal habitat monitoring has traditionally been conducted through satellite and ground-based surveys. Here we investigate the use of UAV-LiDAR (unoccupied aerial vehicle–light detection and ranging) and multispectral photogrammetry to study a Florida coastal wetland. These data have higher resolution than satellite-derived data and are cheaper and faster to collect compared to crewed aircraft or ground surveys. We detected significant canopy change in the period between our survey (2020–2022) and a previous survey (2015), including loss at the scale of individual buttonwood trees (Conocarpus erectus), a woody mangrove associate. The UAV-derived data were collected to investigate the utility of simplified processing and data inputs for habitat classification and were validated with standard metrics and additional ground truth. UAV surveys combined with machine learning can streamline coastal habitat monitoring, facilitating repeat surveys to assess the effects of climate change and other change agents.

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

Understanding where groundwater recharge occurs is essential for managing groundwater resources, especially source-water protection. This can be especially difficult in remote mountainous landscapes where access and data availability are limited. We developed a groundwater recharge potential (GWRP) map across such a landscape based on six readily available datasets selected through the literature review: precipitation, geology, soil texture, slope, drainage density, and land cover. We used field observations, community knowledge, and the Analytical Hierarchy Process to rank and weight the spatial datasets within the GWRP model. We found that GWRP is the highest where precipitation is relatively high, geologic deposits are coarse-grained and unconsolidated, soils are variants of sands and gravels, the terrain is flat, drainage density is low, and land cover is undeveloped. We used GIS to create a map of GWRP, determining that over 83% of this region has a moderate or greater capacity for groundwater recharge. We used two methods to validate this map and assessed it as approximately 87% accurate. This study provides an important tool to support informed groundwater management decisions in this and other similar remote mountainous landscapes.

Mycorrhizal associations of trees play a key role in ecosystem biogeochemistry, yet we know relatively little about the nutrient acquisition strategies of ericoid mycorrhizal (ErM) shrubs, despite their prevalence in forest understories. ErM shrubs tend to produce litter with high concentrations of polyphenols, which form decay‐resistant complexes with organic nitrogen (N). Some ErM fungi appear capable of producing a diverse suite of extracellular enzymes, but it is unclear whether this enables ErM shrubs to acquire the N from their own litter, and whether this ability is greater than that of ectomycorrhizal (EcM) trees, with which they commonly co‐occur. Here, we conducted two growth chamber experiments with ¹⁵N‐enriched root and leaf litters to determine whether EcM pine and ErM vaccinium differ in litter N acquisition from these sources. Grown individually, pine and vaccinium plants acquired 1 mg or 4% of the litter N after one year. Differences between pine and vaccinium in litter N acquisition were correlated with litter chemistry, where vaccinium acquired more N from litter with a high concentration of tannins and a high C:N, while pine acquired more N from litter with a high acid‐insoluble fraction. When plants were grown in pairs, the presence of a vaccinium neighbor reduced the biomass of both species by an average of 24%, but it only reduced litter N acquisition by pine (55%), demonstrating vaccinium's ability to compete with pine for litter N. Regardless of the competitive context, vaccinium allocated a greater percentage (76%) of its acquired litter N to aboveground biomass compared with pine (47%), suggesting vaccinium is more efficient at allocating this N to photosynthesis and growth. Our finding that EcM pine and ErM vaccinium have varying abilities to acquire N from these common litters suggests a degree of niche complementarity, which may facilitate the coexistence of these plants in forest ecosystems. However, our results also suggest that, at high densities, ErM vaccinium has the potential to suppress the growth and N acquisition of EcM pine. Distinct strategies of EcM trees and ErM shrubs for acquiring and using N have the potential to shape biogeochemical cycles of many forested ecosystems.

Between the 1780 and 1980s, more than half of the wetlands in the conterminous US were lost. As wetlands have been lost, numerous artificial water features (AWFs), such as stormwater retention ponds, golf course water features, and reservoirs, have been constructed. We contrasted the loss of wetland area and perimeter to the gain of AWF area and perimeter and further explored how this transformation has altered the spatial characteristics of the waterscape. We conducted this analysis in the Tampa Bay Watershed, a large coastal watershed that lost 33% of its wetland area between the 1950s-2007. Trends have been towards fewer, smaller wetlands and more, smaller AWFs. The loss of wetland area far exceeds the gain in AWF area, leading to an overall loss of 23% of the combined wetland and AWF area. However, the loss of wetland perimeter almost equals the gain in AWF perimeter, leading to an overall loss of just 2% of the combined wetland and AWF perimeter. The loss of wetlands and gain of AWFs have predominantly occurred in different geographic locations, with the loss of wetlands predominantly in the headwaters and the gain in AWFs predominantly adjacent to Tampa Bay. Wetlands became further apart, though generally retained their natural distribution, while AWFs became closer to one another and now mirror the more natural wetland distribution. Overall, the physical structure of the waterscape of today is different than in the past, which likely reflects a change in functions performed and related ecological services provided at local and landscape scales.

Groundwater discharge plays an important role in the hydrologic and ecologic functioning of rivers including sustaining streamflow and related habitat year‐round. Simultaneously, groundwater supports the increasing demands of people as the global population continues to grow. Balancing the needs of users becomes increasingly important as climate change introduces greater uncertainty in water resources and fisheries, especially for economically important anadromous species that depend on freshwater resources. We investigated the seasonal and regional variability of groundwater contributions to six non‐glacial mainstem salmon‐bearing rivers in south‐central Alaska. We hypothesized that groundwater contributes more than half of the annual streamflow, and nearly all of the streamflow during late summer and mid‐winter periods. Our results show that groundwater discharge is the dominant source of streamflow, with an annual relative contribution of 70%. Groundwater contribution to streamflow varies seasonally, ranging from approximately 50%–70% during peak flow to 70%–80% during low flow. Groundwater contribution to streamflow also varies spatially, with maximum differences in groundwater contribution between watersheds ranging ±20 percentage points. However, all rivers in the study area followed similar trends, with higher groundwater contributions in summer and winter and lower contributions in spring and fall.