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Abstract Aim To investigate whether the frequently advocated climate‐matching species distribution modeling approach could predict the well‐characterized colonization of Florida by the Madagascar giant day gecko Phelsuma grandis. Location Madagascar and Florida, USA. Methods To determine the climatic conditions associated with the native range of P. grandis, we used native‐range presence‐only records and Bioclim climatic data to build a Maxent species distribution model and projected the climatic thresholds of the native range onto Florida. We then built an analogous model using Florida presence‐only data and projected it onto Madagascar. We constructed a third model using native‐range presences for both P. grandis and the closely related parapatric species P. kochi. Results Despite performing well within the native range, our Madagascar Bioclim model failed to identify suitable climatic habitat currently occupied by P. grandis in Florida. The model constructed using Florida presences also failed to reflect the distribution in Madagascar by overpredicting distribution, especially in western areas occupied by P. kochi. The model built using the combined P. kochi/P. grandis dataset modestly improved the prediction of the range of P. grandis in Florida, thereby implying competitive exclusion of P. grandis by P. kochi from habitat within the former's fundamental niche. These findings thus suggest ecological release of P. grandis in Florida. However, because ecological release cannot fully explain the divergent occupied niches of P. grandis in Madagascar versus Florida, our findings also demonstrate some degree of in situ adaptation in Florida. Main conclusions Our models suggest that the discrepancy between the predicted and observed range of P. grandis in Florida is attributable to either in situ adaptation by P. grandis within Florida, or a combination of such in situ adaptation and competition with P. kochi in Madagascar. Our study demonstrates that climate‐matching species distribution models can severely underpredict the establishment risk posed by non‐native herpetofauna.

Abstract It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.

Abstract How climate and habitat drive variation in aquatic metabolism in wetlands remains uncertain. To quantify differences in seasonal aquatic metabolism among wetlands, we estimated aquatic ecosystem metabolism (gross primary productivity, GPP; ecosystem respiration, ER; net aquatic productivity, NAP) in subtropical ridge and slough wetlands of the Florida Everglades from more than 2 yr of continuously measured water column dissolved oxygen, photosynthetically active radiation (PAR), water temperature, and water depth. Gross primary productivity and ER were modeled from light, temperature, and water depth using non‐linear minimization and maximum likelihood. Reaeration rates were estimated from wind speed. Dissolved oxygen was below saturation at all sites during both wet and dry seasons. Water depth interacted with vegetation to influence PAR, water temperature, and spatiotemporal patterns in aquatic metabolism. Gross primary productivity and ER were highest at the slough with lowest submerged aquatic vegetation (low‐SAV slough), intermediate in the sawgrass (Cladium jamaicense) ridge site, and lowest at the slough with highest submerged aquatic vegetation (high‐SAV slough). Ecosystem respiration was strongly positively correlated with GPP at the sawgrass ridge and low‐SAV slough sites. Gross primary productivity increased with water temperature and PAR across all habitat types, whereas ER decreased (more respiration) with water temperature and PAR. Net aquatic productivity was negatively correlated with water temperature and positively correlated with PAR, suggesting that ER was more sensitive than GPP to water temperature. Aquatic metabolism was largely net heterotrophic in all wetlands, and high‐SAV appeared to buffer seasonal variation in PAR and water temperatures that drive NAP in subtropical wetlands. Our results suggest that aquatic ecosystem metabolism in wetlands with seasonal hydrology is sensitive to changes in water depth and vegetation density that influence temperature and light. Expanding our understanding of how metabolic processes and carbon cycling in wetland ecosystems vary across gradients in hydrology, vegetation, and organic matter could enhance our understanding and protection of conditions that maximize carbon storage.

Shaped by the hydrology of the Kissimmee‐Okeechobee‐Everglades watershed, the Florida Everglades is composed of a conglomerate of wetland ecosystems that have varying capacities to sequester and store carbon. Hydrology, which is a product of the region's precipitation and temperature patterns combined with water management policy, drives community composition and productivity. As shifts in both precipitation and air temperature are expected over the next 100 years as a consequence of climate change, CO2 dynamics in the greater Everglades are expected to change. To reduce uncertainties associated with climate change and to explore how projected changes in atmospheric CO2 concentration and climate can alter current CO2 exchange rates in Everglades freshwater marsh ecosystems, we simulated fluxes of carbon among the atmosphere, vegetation, and soil using the DAYCENT model. We explored the effects of low, moderate, and high scenarios for atmospheric CO2 (550, 850, and 950 ppm), mean annual air temperature (+1, +2.5, and +4.2°C) and precipitation (−2, +7, and +14%), as predicted by the IPCC for the year 2100 for the region, on CO2 exchange rates in short‐ and long‐hydroperiod wetland ecosystems. Under 100 years of current climate and atmospheric CO2 concentration, Everglades freshwater marsh ecosystems were estimated to be CO2‐neutral. As atmospheric CO2 concentration increased and under climate change projections, there were slight shifts in the start and length of the wet season (−1 to +7 days) and a small enhancement in the sink capacity (by −169 to −573 g C m−2 century−1) occurred at both short‐ and long‐ hydroperiod ecosystems compared to CO2 dynamics under the current climate regime. Over 100 years, rising temperatures increased net CO2 exchange rates (+1 to 13 g C m−2 century−1) and shifts in precipitation patterns altered cumulative net carbon uptake by +13 to −46 g C m−2 century−1. While changes in ecosystem structure, species composition, and disturbance regimes were beyond the scope of this research, results do indicate that climate change will produce small changes in CO2 dynamics in Everglades freshwater marsh ecosystems and suggest that the hydrologic regime and oligotrophic conditions of Everglades freshwater marshes lowers the ecosystem sensitivity to climate change.

Abstract Drought is a global issue that is exacerbated by climate change and increasing anthropogenic water demands. The recent occurrence of drought in California provides an important opportunity to examine drought response across ecosystem classes (forests, shrublands, grasslands, and wetlands), which is essential to understand how climate influences ecosystem structure and function. We quantified ecosystem resistance to drought by comparing changes in satellite‐derived estimates of water‐use efficiency (WUE = net primary productivity [NPP]/evapotranspiration [ET]) under normal (i.e., baseline) and drought conditions (ΔWUE = WUE2014 − baseline WUE). With this method, areas with increasing WUE under drought conditions are considered more resilient than systems with declining WUE. Baseline WUE varied across California (0.08 to 3.85 g C/mm H2O) and WUE generally increased under severe drought conditions in 2014. Strong correlations between ΔWUE, precipitation, and leaf area index (LAI) indicate that ecosystems with a lower average LAI (i.e., grasslands) also had greater C‐uptake rates when water was limiting and higher rates of carbon‐uptake efficiency (CUE = NPP/LAI) under drought conditions. We also found that systems with a baseline WUE ≤ 0.4 exhibited a decline in WUE under drought conditions, suggesting that a baseline WUE ≤ 0.4 might be indicative of low drought resistance. Drought severity, precipitation, and WUE were identified as important drivers of shifts in ecosystem classes over the study period. These findings have important implications for understanding climate change effects on primary productivity and C sequestration across ecosystems and how this may influence ecosystem resistance in the future.