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There is growing evidence of climate change impacts on northern ecosystems. While most climate change studies base their assessments on air temperature, spatial variation of soil temperature responses have not been fully examined as a metric of climate change. Here we examined spatial variations of soil temperature responses to an ensemble of regional climate model (RCM) projections at multiple depths in upland and riparian zones in the Swedish boreal forest. Modeling showed a stronger influence of air temperature on riparian soil temperature than was simulated for upland soils. The RCM ensemble projected a warming range of 4.7–6.0 °C in riparian and 4.3–5.7 °C in upland soils. However, soils were slightly colder in the riparian zone during winter. While the historical record showed that upland soils are about 0.4 °C warmer than the riparian soils, this may be reversed in the future as model projections showed that on an annual basis, riparian soils might be slightly warmer by 0.2 to 0.4 °C than upland soils. However, upland soils could warm up earlier (April) compared to riparian soils (May).

Puerto Rico is subject to frequent and severe impacts from hurricanes, whose long-term ecological role must be assessed on a scale of centuries. In this study we applied a method for reconstructing hurricane disturbance regimes developed in an earlier study of hurricanes in New England. Patterns of actual wind damage from historical records were analyzed for 85 hurricanes since European settlement in 1508. A simple meteorological model (HURRECON) was used to reconstruct the impacts of 43 hurricanes since 1851. Long-term effects of topography on a landscape scale in the Luquillo Experimental Forest (LEF) were simulated with a simple topographic exposure model (EXPOS). Average return intervals across Puerto Rico for F0 damage (loss of leaves and branches) and F1 damage (scattered blowdowns, small gaps) on the Fujita scale were 4 and 6 years, respectively. At higher damage levels, a gradient was created by the direction of the storm tracks and the weakening of hurricanes over the interior mountains. Average return intervals for F2 damage (extensive blowdowns) and F3 damage (forests leveled) ranged from 15 to 33 years and 50 to 150 years, respectively, from east to west. In the LEF, the combination of steep topography and constrained peak wind directions created a complex mosaic of topographic exposure and protection, with average return intervals for F3 damage ranging from 50 years to >150 years. Actual forest damage was strongly dependent on land-use history and the effects of recent hurricanes. Annual and decadal timing of hurricanes varied widely. There was no clear centennial-scale trend in the number of major hurricanes over the historical period.

Questions: Can small and isolated high-conservation value forests (e. g. designated woodland key habitats) maintain old-growth forest characteristics and functionality in fragmented landscapes? To what extent have past disturbances (natural and anthropogenic) influenced the development of old-growth characteristics of these forests? How long does it take for selectively cut stands to attain conditions resembling old-growth forests? Location: Southern boreal zone of central Sweden. Methods: We linked multiple lines of evidence from historical records, biological archives, and analyses of current forest structure to reconstruct the forest history of a boreal landscape, with special emphasis on six remaining core localities of high-conservation value forest stands. Results: Our reconstructions revealed that several of these stands experienced wildfires up to the 1890s; all had been selectively harvested in the late 1800s; and all underwent substantial structural and compositional reorganization over the following 100-150 years. This time interval was sufficient to recover considerable amounts of standing and downed dead wood (mean 60.3 m³ ha⁻¹ ), a range of tree ages and sizes (mean basal area 32.6 m² ha⁻¹), and dominance of shade-tolerant spruce. It was insufficient to obtain clearly uneven tree age structures and large (> 45cm diameter) living and dead trees. Thus, these forests contain some, but not all, important compositional and structural attributes of old-growth forests, their abundance being dependent on the timing and magnitude of past natural and anthropogenic disturbances. Our landscape-level analysis showed marked compositional and structural differences between the historical forest landscape and the present landscape, with the latter having a greater proportion of young forests, introduction of nonnative species, and lack of large trees and dead wood. Conclusions: The remnant high-conservation value stands were not true representatives of the pre-industrial forests, but represent the last vestige of forests that have regenerated naturally and maintained a continuous tree cover. These traits, coupled with their capacity for old-growth recovery, make them valuable focal areas for conservation.

A comprehensive study on a 350-cm long sediment core revealed rich geochemical information in an interesting location of Xiangxi He (river) in TGR. The vertical profiles of trace metals (TMs) and nutrients displayed an irregular pattern, fluctuating around their background values, and no gradual increase was detectable. For TMs, average concentrations were Cd: 0.35 ± 0.21, Co: 21.91 ± 3.30, Cr: 27.06 ± 5.03, Cu: 27.09 ± 4.96, Ni: 34.08 ± 5.23, Pb: 20.58 ± 5.84 and Zn: 80.18 ± 69.16, all in μg/g (n = 61). For most of TMs, their bioavailable forms were only a minor fraction. Therefore, the conventional total metal concentrations will inevitably overestimate their potential risk to ecosystem. For nutrients, average concentrations were total phosphorus (TP): 0.81 ± 0.14, total nitrogen (TN): 1.46 ± 0.28, total sulfur (TS): 0.54 ± 0.19, total carbon (TC): 32.02 ± 8.35, total organic carbon (TOC): 20.90 ± 7.5, all in g·kg−1 (n = 61), and appeared somewhat higher than the “lowest effect level”, which seems to explain the repeated observations of algal blooms in this relatively stagnant section of the river in TGR. The mineralogical composition identified by XRD agreed well with that obtained by chemical analyses. The study revealed that the Xiangxi sediment core (XX06) was remarkably heterogeneous in mineralogical composition, probably caused by natural processes and the dam management. The increased sedimentation rate within TGR should induce a mass dilution effect, thus decreasing the concentrations of pollutants. Large sampling scale and a universally accepted and uniformed analytical sampling and method, together with more vigorous analytical quality control, must be practiced and a solid database be built to facilitate the ecosystem assessment of TGR area.

Climate change affects all segments of the agricultural enterprise, and there is mounting evidence that the continuing warming trend with shifting seasonality and intensity in precipitation will increase the vulnerability of agricultural systems. Agricultural is a complex system within the USA encompassing a large number of crops and livestock systems, and development of indicators to provide a signal of the impact of climate change on these different systems would be beneficial to the development of strategies for effective adaptation practices. A series of indicators were assembled to determine their potential for assessing agricultural response to climate change in the near term and long term and those with immediate capability of being implemented and those requiring more development. The available literature reveals indicators on livestock related to heat stress, soil erosion related to changes in precipitation, soil carbon changes in response to increasing carbon dioxide and soil management practices, economic response to climate change in agricultural production, and crop progress and productivity. Crop progress and productivity changes are readily observed data with a historical record for some crops extending back to the mid-1800s. This length of historical record coupled with the county-level observations from each state where a crop is grown and emerging pest populations provides a detailed set of observations to assess the impact of a changing climate on agriculture. Continued refinement of tools to assess climate impacts on agriculture will provide guidance on strategies to adapt to climate change.

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem.

Historical biodiversity occurrence records are often discarded in spatial modeling analyses because of a lack of a method to quantify their sampling bias. Here we propose a new approach for predicting sampling bias in historical written records of occurrence, using a South African example as proof of concept. We modelled and mapped accessibility of the study area as the mean of proximity to freshwater and European settlements. We tested the model's ability to predict the location of historical biodiversity records from a dataset of 2612 large mammal occurrence records collected from historical written sources in South Africa in the period 1497–1920. We investigated temporal, spatial and environmental biases in these historical records and examined if the model prediction and occurrence dataset share similar environmental bias. We find a good agreement between the accessibility map and the distribution of sampling effort in the early historical period in South Africa. Environmental biases in the empirical data are identified, showing a preference for lower maximum temperature of the warmest month, higher mean monthly precipitation, higher net primary productivity and less arid biomes than expected by a uniform use of the study area. We find that the model prediction shares similar environmental bias as the empirical data. Accessibility maps, built with very simple statistical rules and in the absence of empirical data, can thus predict the spatial and environmental biases observed in historical biodiversity occurrence records. We recommend that this approach be used as a tool to estimate sampling bias in small datasets of occurrence and to improve the use of these data in spatial analyses in ecological and conservation studies.

Recent research has demonstrated the important role that high-biomass species play in the transfer of energy and nutrients across habitat boundaries, as well as the ecosystem consequences of their loss. To contrast the historical and current biomass of historically abundant anadromous forage fish, we combined historical records of habitat loss from damming with contemporary freshwater productivity of alewives and diet data of freshwater predator fish. Significant declines in production occurred by 1850 in the northeastern United States, long before any direct abundance data were available, which would have had significant effects on freshwater prey resources for the numerous predators directly affected by the transfer of nutrients across the freshwater–marine nexus. Current freshwater systems operate at approximately 6.7% of historical capacity of anadromous alewife biomass and abundance. This provides an example of habitat-mediated changes in connectivity limiting nutrient flux and energy flow among populations and species that alter ecosystem function at multiple scales.

Drought is a complex and multivariate phenomenon influenced by diverse physical and biological processes. Such complexity precludes simplistic explanations of cause and effect, making investigations of climate change and drought a challenging task. Here, we review important recent advances in our understanding of drought dynamics, drawing from studies of paleoclimate, the historical record, and model simulations of the past and future. Paleoclimate studies of drought variability over the last two millennia have progressed considerably through the development of new reconstructions and analyses combining reconstructions with process-based models. This work has generated new evidence for tropical Pacific forcing of megadroughts in Southwest North America, provided additional constraints for interpreting climate change projections in poorly characterized regions like East Africa, and demonstrated the exceptional magnitude of many modern era droughts. Development of high resolution proxy networks has lagged in many regions (e.g., South America, Africa), however, and quantitative comparisons between the paleoclimate record, models, and observations remain challenging. Fingerprints of anthropogenic climate change consistent with long-term warming projections have been identified for droughts in California, the Pacific Northwest, Western North America, and the Mediterranean. In other regions (e.g., Southwest North America, Australia, Africa), however, the degree to which climate change has affected recent droughts is more uncertain. While climate change-forced declines in precipitation have been detected for the Mediterranean, in most regions, the climate change signal has manifested through warmer temperatures that have increased evaporative losses and reduced snowfall and snowpack levels, amplifying deficits in soil moisture and runoff despite uncertain precipitation changes. Over the next century, projections indicate that warming will increase drought risk and severity across much of the subtropics and mid-latitudes in both hemispheres, a consequence of regional precipitation declines and widespread warming. For many regions, however, the magnitude, robustness, and even direction of climate change-forced trends in drought depends on how drought is defined, with often large differences across indicators of precipitation, soil moisture, runoff, and vegetation health. Increasing confidence in climate change projections of drought and the associated impacts will likely depend on resolving uncertainties in processes that are currently poorly constrained (e.g., land-atmosphere interactions, terrestrial vegetation) and improved consideration of the role for human policies and management in ameliorating and adapting to changes in drought risk.

We examine long-term trends in the historical record of extreme precipitation events occurring over the French Mediterranean area. Extreme events are considered in terms of their intensity, frequency, extent and precipitated volume. Changes in intensity are analysed via an original statistical approach where the annual maximum rainfall amounts observed at each measurement station are aggregated into a univariate time-series according to their dependence. The mean intensity increase is significant and estimated at + 22% (+ 7 to + 39% at the 90% confidence level) over the 1961–2015 period. Given the observed warming over the considered area, this increase is consistent with a rate of about one to three times that implied by the Clausius–Clapeyron relationship. Changes in frequency and other spatial features are investigated through a Generalised Linear Model. Changes in frequency for events exceeding high thresholds (about 200 mm in 1 day) are found to be significant, typically near a doubling of the frequency, but with large uncertainties in this change ratio. The area affected by severe events and the water volume precipitated during those events also exhibit significant trends, with an increase by a factor of about 4 for a 200 mm threshold, again with large uncertainties. All diagnoses consistently point toward an intensification of the most extreme events over the last decades. We argue that it is difficult to explain the diagnosed trends without invoking the human influence on climate.