The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.

Recent advancements in understanding remotely sensed solar-induced chlorophyll fluorescence often suggest a linear relationship with gross primary productivity at large spatial scales. However, the quantum yields of fluorescence and photochemistry are not linearly related, and this relationship is largely driven by irradiance. This raises questions about the mechanistic basis of observed linearity from complex canopies that experience heterogeneous irradiance regimes at subcanopy scales. We present empirical data from two evergreen forest sites that demonstrate a nonlinear relationship between needle-scale observations of steady-state fluorescence yield and photochemical yield under ambient irradiance. We show that accounting for subcanopy and diurnal patterns of irradiance can help identify the physiological constraints on needle-scale fluorescence at 70-80% accuracy. Our findings are placed in the context of how solar-induced chlorophyll fluorescence observations from spaceborne sensors relate to diurnal variation in canopy-scale physiology.Plain Language Summary Chlorophyll fluorescence is a faint signal emitted by plants that can provide information about photosynthesis and other processes important for plant growth. However, fluorescence is governed by complex chemical reactions that depend on light, and it is not linearly related to photosynthetic carbon uptake. Ecosystems with complex canopy structure, such as evergreen needleleaf forests, experience dynamic sunlit and shaded conditions, which make fluorescence observations challenging to interpret. However, by accounting for incoming light at fine spatial scales in studies using fluorescence, we can track the conditions under which canopies are partitioned by light-saturated and light-limited physiological constraints at 70-80% accuracy. Findings from our field-based study are relevant for interpreting satellite-based measurements of fluorescence as a proxy of photosynthetic carbon uptake. Furthermore, our study underscores the need for further research on how data from leaf-scale studies can be scaled up to shed light on ecosystem responses to changing climatic conditions.

Operationalizing integrated water resource management (IWRM) often involves decentralization of water management via community-based management (CBM). While attention has been given to the components leading to successful CBM, less is known about what factors motivate people's willingness to participate (WTP) in such programs. This study analyzed factors that influence household WTP in CBM in a transboundary watershed located where El Salvador, Guatemala, and Honduras converge – the Trifinio Region. Several variables were hypothesized to influence WTP: sense of community (SOC), dependence on water resources, level of concern for water resources, and socio-economic characteristics. In 2014, quantitative and qualitative data were collected from 62 households in five communities. Most respondents reported high levels of WTP in future CBM initiatives, and multivariate regression analysis revealed that SOC was the most important predictor of WTP, with wealth and perceptions of watershed management also statistically significant. Qualitative analyses revealed water availability was more concerning than water quality, and perceptions of inequitable access to water is an important constraint to developing CBM strategies. Taken together, these results suggest that enhancing SOC and relationships between local and regional levels of governance prior to establishing community-based projects would facilitate more success in implementing IWRM.

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.