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Understanding historic patterns of land use and land cover change across large temporal and spatial scales is critical for developing effective biodiversity conservation management and policy. We quantify the extent and fragmentation of suitable habitat across the continental range of Asian elephants ( Elephas maximus ) based on present-day occurrence data and land-use variables between 850 and 2015 A.D. We found that following centuries of relative stability, over 64% (3.36 million km 2 ) of suitable elephant habitat across Asia was lost since the year 1700, coincident with colonial-era land-use practices in South Asia and subsequent agricultural intensification in Southeast Asia. Average patch size dropped 83% from approximately 99,000–16,000 km 2 and the area occupied by the largest patch decreased 83% from ~ 4 million km 2 (45% of area) to 54,000 km 2 (~ 7.5% of area). Whereas 100% of the area within 100 km of the current elephant range could have been considered suitable habitat in the year 1700, over half was unsuitable by 2015, driving potential conflict with people. These losses reflect long-term decline of non-forested ecosystems, exceeding estimates of deforestation within this century. Societies must consider ecological histories in addition to proximate threats to develop more just and sustainable land-use and conservation strategies.

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and 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.

Islands are hotspots of biological and cultural diversity that face growing threats from invasive species and climate change. Invasive mammal eradication on islands is a proven conservation intervention that prevents biodiversity loss and is a foundational activity for restoring degraded island-ocean ecosystems. However, these interventions are prioritized and evaluated primarily on biodiversity-based objectives despite growing evidence that invasive species removal may also serve as an effective nature-based solution to increase climate resilience of island-ocean ecosystems and contribute to climate change solution by protecting and restoring unique carbon stocks of native woody vegetation. To assess the effectiveness of interventions at the global scale, we developed a consistent and scalable framework for the long-term monitoring of tree cover, forest extent, forest carbon, and vegetation productivity in 1078 islands across 17 ecozones. Time-series of satellite-derived estimates of tree cover and the Normalized Difference Vegetation Index over 36 yr (1984–2020) were used to establish annual baselines and changes in forest extent, carbon stocks, and vegetation productivity. The analysis revealed significant and sustained positive trends in all the indices on islands with eradication. The magnitude and potential biological relevance of these effects was highly variable across ecozones, but the overall sustained effects provide strong evidence of a positive ecosystem response to invasive mammal removal. We also found that, collectively, these islands sustain more than 940 000 ha of forest and 53 million MgC of forest carbon. This novel framework enables measuring the climate co-benefits of island restoration interventions in relevant policy terms using a low cost and globally consistent methodology that is applicable across the range of spatial and temporal scales pertinent to ecosystem recovery dynamics on islands.

In 2019, the Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive (after May 1) to promote springtime biomass accumulation. We used satellite imagery calibrated with springtime in situ measurements collected from 2006–2021 (n = 722) to derive biomass estimates for Maryland fields planted to cereal cover crop species (286,200 ha total over two seasons). Cover crop C content remained steady throughout the cover crop growing season (42.6% of biomass), whereas N concentration had an inverse relationship with biomass and ranged from 1.7 to 2.9%. Throughout Maryland, delayed termination fields (n = 19,120; average termination of May 18) were, on average, estimated to accumulate an additional 789 kg of biomass, 15 kg of N, and 336 kg of C per hectare when compared to fields associated with standard termination dates (n = 28,811; average termination of April 16). Over two cover crop seasons (2019–2021), the delayed termination incentive yielded an extra 75,660,000 kg biomass, 1,526,000 kg N, and 32,230,000 kg C across 96,040 hectares. Regularly terminated field incentives cost an average of US $0.10 per kg of biomass and $ 4.09 per kg of N, with variability associated with agronomic management (species, planting method). Delayed termination fields cost of$0.08 per kg of biomass and $ 3.51 per kg of N. Late‐planted cover crops that were terminated early had minimal environmental benefit, and wheat, which comprised 68% of cover crop area, performed poorly compared with other cereal species. Our findings demonstrate that substantial additional springtime biomass, C, and N accumulation were achieved through the delayed termination incentive. Core Ideas Remote sensing, combined with destructive sampling, can enable the estimation of winter cover crop biomass at scale. Winter cover crop fields that delay termination showed higher biomass, N, and C accumulation. Fields that received an incentive to delay termination had, on average, lower cost per unit performance. Performance varied by species, planting date, planting method, and termination date.

PurposeCover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy.MethodsThe objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (n = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches.ResultsModels using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation.ConclusionAs spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.

Winter cover crops encompass a range of species planted in late summer and fall for a variety of reasons relating to soil health, nutrient retention, soil compaction, biotic diversity, and erosion prevention. As agricultural intensification continues, the practice of winter cover cropping remains a crucial practice to reduce leaching from agricultural fields. Maryland and Delaware both incentivize cover cropping to meet water quality objectives in the Chesapeake Bay Watershed. These large-scale programs necessitate methods to evaluate cover crop performance over the landscape. Cover crop quantity and quality was measured at 2,700 locations between 2006–2021 with a focus on fields planted to four cereal species: wheat, rye, barley, and triticale. Samples were GPS located and timed with satellite remote sensing observations from SPOT 4, SPOT 5, Landsat 5, Landsat 7, Landsat 8, or Sentinel-2. When paired imagery at 10-30 m spatial resolution , there is a strong relationship between the normalized difference vegetation index (NDVI) and percent ground cover (R2 = 0.72) as well as NDVI and biomass (as high as R2 = 0.77). There is also a strong relationship between Δ Red Edge (a combination of 740 nm and 783 nm bands) and nitrogen content (R2 = 0.75). These equations were applied to Harmonized Landsat Sentinel-2 products and used to estimate cover crop aboveground biomass in ~300,000 ha of Maryland Department of Agricultures and ~60,000 ha of Delaware Association of Conservation Districts enrolled fields from 2019–2021 and grouped by agronomic method. Wintertime and springtime cover crop biomass varied based on planting date, planting method, species, termination date, and termination method. Early planted fields had higher wintertime biomass while fields that delayed termination had higher springtime biomass. Triticale had consistently higher biomass while wheat had the lowest biomass. Fields planted using a drill followed by light tillage or no-till drill had higher biomass, likely due to the better seed-to-soil contact. Fields that were taken to harvest or terminated for on farm use (roller crimped, green chopped) also had higher springtime biomass than other termination methods. Incentives can be used to encourage specific agronomic methods and these findings can be used to inform adaptive management in the Mid-Atlantic Region.

Several biogeographic studies of salamanders have described relationships between salamander body size and climate. We specifically selected Plethodon cinereus as a widely distributed species that was well represented in museum collections to investigate the effects of warming climate on adult body size. We found a positive correlation between mean maximum July temperature and body size, and a negative correlation between precipitation of the driest month and body size. Surface‐collected adult P. cinereus were 2.3% larger in warmer counties on the coastal plain compared with cooler counties in the Appalachian Mountains. We compared salamanders collected between 1950 and 1970 versus those collected between 1980 and 2000 and found that body size increased 1.8% in counties on the coastal plain where mean maximum July temperatures had also increased by 0.5–1.2 °C. Warming temperatures alone, however, did not adequately account for the observed size increases, because body size also increased 1.3% in places that experienced less than 0.25 °C warming, but that difference was not statistically significant. We compared adult redback salamander body sizes using extensive museums collections and found that salamanders were larger with warmer, drier conditions. These effects were most pronounced on the coastal plain compared to counties in the central Appalachians. e00031

Habitat loss drives species' declines worldwide, but is seldom quantified over centennial timescales. We constructed ecological niche models for Asian elephants based on land-use change between 850-2015, and predictions under six different climate/socioeconomic scenarios from 2015-2099. We find that over 64% of suitable natural habitat across diverse ecosystems was lost over the past three centuries. Average patch size dropped 83% from approximately 99,000 km2 to 16,000 km2 and the area occupied by the largest patch decreased 83% from ~ 4 million km2 (45% of area) to 54,000 km2 (~7.5% of area). Over half of current elephant range appears unsuitable. Habitat availability is predicted to decline further this century across all scenarios. The most severe losses occur under RCP3.4-SSP4, representing mid-range emissions but high regional inequities. We conclude that climate change mitigation measures must include policies to ensure inter-regional socioeconomic equity to safeguard landscapes for elephants, humans, and other species. Competing Interest Statement The authors have declared no competing interest. Footnotes * Introduction updated to clarify that original sampling locations represent suitable natural habitat that does not contain heavy anthropogenic activity; Figure 1 updated to extend analyses back to the year 850; Methods, results and supplementary text provides more detail on future scenarios; discussion addresses limitations with definition of natural habitats and modelling studies; new supplementary figure S3 shows suitable habitat in year 2015 scenarios in the extant range and buffer regions at 25-100km; new supplementary figure S6 shows suitable habitat in range+buffer by year 2099 under six scenarios; figure S7 shows fragmentation patterns in the future under a different binarization threshold value.

A 4-year-old neutered male cat was presented with a 2-month history of intermittent constipation that progressed to obstipation. Primary clinical findings included a large, multi lobulated mass in the caudodorsal abdomen, peripheral eosinophilia and hyperglobulinemia. Abdominal imaging revealed a multilobulated, cavitated mass in the sublumbar region. Exploratory celiotomy revealed multiple firm masses in the sublumbar retroperitoneal space causing ventral displacement and compression of the descending colon with extension of the masses into the pelvic canal. Histopathology was consistent with feline gastrointestinal eosinophilic sclerosing fibroplasia (FGESF). Aerobic culture was positive for Staphylococcus aureus. The cat was treated with prednisolone (2 mg/kg PO q24h), lactulose (0.5 g/kg PO q8h), amoxicillin/clavulanic acid (62.5 mg/cat PO q12h for 1 month) and fenbendazole (50 mg/kg PO q24h for 5 days). Six months postoperatively, the cat had no recurrence of clinical signs. Repeat evaluation and imaging at day 732 postoperatively revealed marked improvement of the abdominal mass, resolution of peripheral eosinophilia and no clinical signs with continued prednisolone therapy (0.5 mg/kg PO q24h). This is a report of a primary extramural FGESF lesion, and the first description of characteristics of FGESF on CT. Previous evidence suggests that the most favorable outcomes require immunosuppressive therapy and complete surgical excision; however, this case demonstrates a favorable outcome with medical management alone.