The potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

Since the mid-1800s pinyon-juniper (PJ) woodlands have been encroaching into sagebrush-steppe shrublands and grasslands such that they now comprise 40% of the total forest and woodland area of the Intermountain West of the United States. More recently, PJ ecosystems in select areas have experienced dramatic reductions in area and biomass due to extreme drought, wildfire, and management. Due to the vast area of PJ ecosystems, tracking these changes in woodland tree cover is essential for understanding their consequences for carbon accounting efforts, as well as ecosystem structure and functioning. Here we present a carbon monitoring, reporting, and verification (MRV) system for characterizing total aboveground biomass stocks and flux of PJ ecosystems across the Great Basin. This is achieved through a two-stage remote sensing approach by first using spatial wavelet analysis to rapidly sample tree cover from very high-resolution imagery (1 m), and then training a Random Forest model which maps tree cover across the region from 2000 to 2016 using temporally-segmented Landsat spectral indices obtained from the LandTrendr algorithm in Google Earth Engine. Estimates of cover were validated against field data from the SageSTEP project (R ² = 0.67, RMSE = 10% cover). Biomass estimated from cover-based allometry was higher than estimates from the Forest Inventory and Analysis Program (FIA) at the plot-level (bias = 5 Mg ha⁻¹ and RMSE = 15.5 Mg ha⁻¹) due in part to differences in tree-level biomass allometrics. County-level aggregation of biomass closely matched estimates from the FIA (R ² = 0.97) after correcting for bias at the plot level. Even after many previous decades of encroachment, we find forest area (i.e. areas with ≥10% cover) increasing at a steady rate of 0.46% per year, but 80% of the 9.86 Tg increase in biomass is attributable to infilling of existing forest. This suggests that the known consequences of encroachment such as reduced water availability, impacts to biodiversity, and risk of severe wildfire may have been increasing across the region in recent years despite the actions of sagebrush steppe restoration initiatives.

Although phosphorus (P) is an essential nutrient for biological productivity, it can cause freshwater degradation when present at fairly low concentrations. Monitoring studies using continuous sampling is crucial for documenting P dynamics in freshwater ecosystems and to reduce the risk of eutrophication. Despite literature updates of developments of the analytical methods for measurement of P species in natural waters, there has been no comprehensive review addressing freshwater sample collection, sample preparation, and sample treatment to fractionate and characterize different forms of P. Therefore, this paper aims to elaborate the different techniques for freshwater sampling and to introduce alternative laboratory methods for sample preservation and P fractionation. The advantages and disadvantages of various sampling techniques, including the traditional manual and the recently developed automatic and passive methods, are presented to highlight the importance of collecting representative freshwater samples. Furthermore, we provide suggestions for sample pretreatment, including filtration, transportation, and storage steps to minimize microbial activity and to maximize the accuracy of measurement of various P fractions. Finally, the most common laboratory methods to measure dissolved and particulate as well as the organic and inorganic freshwater P fractions are efficiently provided. Using this guide, a comprehensive monitoring program of P dynamics in freshwater ecosystems can be developed and applied to improve water quality, particularly of P-rich freshwaters.

The diameters and heights of trees are two of the most important components in a forest inventory. In some circumstances, the heights of trees need to be estimated due to the time and cost involved in measuring them in the field. Artificial intelligence models have many advantages in modeling nonlinear height–diameter relationships of trees, which sometimes make them more useful than empirical models in estimating the heights of trees. In the present study, the heights of trees in uneven-aged and mixed stands in the high elevation forests of northern Iran were estimated using an artificial neural network (ANN) model, an adaptive neuro-fuzzy inference system (ANFIS) model, and empirical models. A systematic sampling method with a 150 × 200 m network (0.1 ha area) was employed. The diameters and heights of 516 trees were measured to support the modeling effort. Using 10 nonlinear empirical models, the ANN model, and the ANFIS model, the relationship between height as a dependent variable and diameter as an independent variable was analyzed. The results show, according to R², relative root mean square error (RMSE), and other model evaluation criteria, that there is a greater consistency between predicted height and observed height when using artificial intelligence models (R² = 0.78; RMSE (%) = 18.49) than when using regression analysis (R² = 0.68; RMSE (%) = 17.69). Thus, it can be said that these models may be better than empirical models for predicting the heights of common, commercially-important trees in the study area.

The Zagros region of western Iran has been affected by the recent changes both in amount and in structure of forest cover. We evaluated the influence of several driving forces on forest cover and structure, including socioeconomic (urban and rural population and rural income) and climatic (mean annual rainfall and mean annual temperature) variables. We acquired all time series Landsat images of a study site from 1972 to 2009. The images were classified to produce a land cover map of each year. We calculated landscape and patch metrics and identified independent sets of metrics to describe forest change. The most influential driving force for forest loss was urban population and climatic variables to some extent. These challenges should be addressed at broader scales than those associated with the livelihoods and traditional management of local communities. Rural population had a significant association with landscape structure which should be considered in designing restoration projects.