Ecoregion Scale Research Articles

The relationship between biodiversity and wetland cover varies across regions of the conterminous United States.

Identifying the factors that determine the spatial distribution of biodiversity is a major focus of ecological research. These factors vary with scale from interspecific interactions to global climatic cycles. Wetlands are important biodiversity hotspots and contributors of ecosystem services, but the association between proportional wetland cover and species richness has shown mixed results. It is not well known as to what extent there is a relationship between proportional wetland cover and species richness, especially at the sub-continental scale. We used the National Wetlands Inventory (NWI) to model wetland cover for the conterminous United States and the National Land Cover Database to estimate wetland change between 2001 and 2011. We used a Bayesian spatial Poisson model to estimate a spatially varying coefficient surface describing the effect of proportional wetland cover on the distribution of amphibians, birds, mammals, and reptiles and the cumulative distribution of terrestrial endemic species. Species richness and wetland cover were significantly correlated, and this relationship varied both spatially and by taxonomic group. Rather than a continental-scale association, however, we found that this relationship changed more closely among ecoregions. The species richness of each of the five groups was positively associated with wetland cover in some or all of the Great Plains; additionally, a positive association was found for mammals in the Southeastern Plains and Piedmont of the eastern U.S. Model results indicated negative association especially in the Cold Deserts and Northern Lakes & Forests of Minnesota and Wisconsin, though these varied greatly between groups. Our results highlight the need for wetland conservation initiatives that focus efforts at the level II and III ecoregional scale rather than along political boundaries.

OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

Improving sustainable yield estimates for tropical reef fisheries

AbstractFishing sustainably is a fundamental problem in tropical regions where diverse fisheries and scarce fisheries information challenges efforts to make reliable estimates and associated policies. To improve evaluations and decisions, we compared the predictions of six surplus production models calibrated using various permutations of fisheries‐dependent data with a benchmark model. The benchmark model was built from fisheries‐independent estimates of r and K, tested against rates of change in Kenyan reef fisheries and found to be accurate. Comparisons with the benchmark model were made with fisheries‐dependent equilibrium and non‐equilibrium models, fixing or not fixing r and K, pooled versus site averaged solutions, and rising, falling, and pooled fishing effort over time. Evaluations indicate high variability in MMSY predictions and notable overestimates of MMSY (~75%) and effort (~210%) for Fox and Schaefer equilibrium models. Non‐equilibrium models had high failure rates (~25%) but successful fits performed better and indicated smaller overestimates (16%) for site‐level evaluations. The Pella–Tomlinson model was most accurate (MMSY = 5.6 ± 0.60 (SD) tonnes/km2/year) and best‐fit r‐K relationships also aligned well with ecoregional data on K and short‐term yields. Future efforts are advised to pool site data, use conservative recruitment values (z = 0.8), and collect data across times of both rising and falling effort. Recommended methods and subsequent adjustments of the benchmark model should improve local and ecoregional scale MMSYs. The benchmark model was calibrated to estimate MMSY in fished seascapes, but to conserve species with slower life histories, we suggest modifications to limit MMSYs to between 1.8 and 3.2 tonnes/km2/year.

Suitability of Natural Areas for Representing Ecological Change in the Pacific Northwest

Natural areas are tracts of land with little or no evidence of past human influence and designated for research, education, and conservation. Many sites were selected to represent high-quality examples of both common and rare plant association groups. However, the extent to which natural areas characterize regional environmental conditions or gradients important for measuring and understanding the effects of climate change has not been examined. We compared the current collection of natural areas in Oregon and Washington to the broader natural ecosystems found in the region using four ecological parameters derived from existing datasets: forest structure, dominant tree species, vegetation formation classes, and elevation. We evaluated these data sets at both the regional and ecosystem scales and looked at the influence of land ownership in representing these parameters. Our results suggest that the Pacific Northwest natural areas network is well representative of all four parameters at the regional level. There were some gaps in representation at the ecoregion scale and across some land ownerships. Results from this study further support using natural areas for monitoring long-term climate change effects in the Pacific Northwest.

The importance of natural versus human factors for ecological conditions of streams and rivers

Streams are influenced by watershed-scale factors, such as climate, geology, topography, hydrology, and soils, which mostly vary naturally among sites, as well as human factors, agriculture and urban development. Thus, natural factors could complicate assessment of human disturbance. In the present study, we use structural equation modeling and data from the 2008–2009 United States National Rivers and Streams Assessment to quantify the relative importance of watershed-scale natural and human factors for in-stream conditions. We hypothesized that biological condition, represented using a diatom multimetric index (MMI), is directly affected by in-stream physicochemical environment, which in turn is regulated by natural and human factors. We evaluated this hypothesis at both national and ecoregion scales to understand how influences vary among regions. We found that direct influences of in-stream environment on diatom MMIs were greater than natural and human factors at the national scale and in all but one ecoregion. Meanwhile, in-stream environments were jointly explained by natural variations in precipitation, base flow index, hydrological stability, % volcanic rock, soil water table depth, and soil depth and by human factors measured as % crops, % other agriculture, and % urban land use. The explained variance of in-stream environment by natural and human factors ranged from 0.30 to 0.75, for which natural factors independently accounted for the largest proportion of explained variance at the national scale and in seven ecoregions. Covariation between natural and human factors accounted for a higher proportion of explained variance of in-stream environment than unique effects of human factors in most ecoregions. Ecoregions with relatively weak effects by human factors had relatively high levels of covariance, high levels of human disturbance, or small ranges in human disturbance. We conclude that accounting for effects of natural factors and their covariation with human factors will be important for accurate ecological assessments.

Historical processes constrain metacommunity structure by shaping different pools of invertebrate taxa within the Orinoco basin

AbstractAimTo identify and characterize the distribution of invertebrate taxa in the Orinoco basin and how their distribution affects the metacommunity structure along the river network.LocationMeta and Guaviare sub‐basins, Orinoco basin, Colombia, South America.MethodsWe characterized the invertebrate communities and environmental characteristics of 25 streams from six ecoregions in the Orinoco basin. The ecoregions present different historical evolution, altitude, slope, climate and vegetation features. We used multiplicative diversity partitioning to compare the contributions of riffle (α and β1), stream (β2) and ecoregional (β3) scales to the overall gamma diversity of the basin. We also applied the Elements of Metacommunity Structure framework to delineate metacommunity types and a distance‐based redundancy analysis to assess the relative relevance of environmental, spatial and ecoregional drivers in species compositions of invertebrate communities.ResultsStreams showed significant differences in community composition among the ecoregions. Several discrete pools of invertebrate taxa occurred in the basin that largely matched the distribution of the ecoregions. Consequently, the metacommunity in the basin resembled a Clementsian idealized structure. The species composition of invertebrate communities was mostly explained by ecoregion type and its interaction with the local environment, particularly its physiographic features.Main conclusionsHistorical and evolutionary processes have resulted in species pools differing between the ecoregions of the Orinoco basin. At the basin scale, the metacommunity structure seems to be constrained by ecoregional features rather than by spatial structure or the local environment. Hence, using the basin as a unit for biodiversity conservation and river management would not adequately reflect the diversity and distribution patterns in highly heterogeneous basins such as the Orinoco basin.

Assessing the gap between conservation need and protection status for select rare ecosystems in Alaska

AbstractRare ecosystems support unique assemblages of flora and fauna within a small geographic area. As such, their conservation represents an effective method of biodiversity protection. The description, mapping, and assessment of rare ecosystems is a necessary and initial conservation action, yet this has not been completed for Alaska. Here, we provide the first comprehensive treatment of rare terrestrial ecosystems for the state. Thirty‐five rare systems, representing different levels of ecological organization and geographic scale, are presented. In addition, a gap analysis was conducted to evaluate the systems' current level of land management protection relative to their conservation need. Eleven of the mapped ecosystems are considered adequately protected, two are moderately protected, and 22 are less protected. Conservation ranks are incongruously aligned with land management protection levels such that the rarest systems are often not well protected and the less‐imperiled systems are often well protected. On the ecoregion scale, systems with arctic distributions are less protected than are those with boreal and maritime distributions. This rare ecosystem assessment complements species‐ and landscape‐scale conservation studies previously completed for Alaska. Collectively, the recommendations from these assessments provide a science‐based strategy for biological conservation in a vulnerable region of the world.

Projecting impacts of climate change on global terrestrial ecoregions

Terrestrial ecoregions, including critical ecoregions (CEs), vulnerable ecoregions (VEs), and intact ecoregions (IEs) have been used by the World Wildlife Fund (WWF) to classify global biodiversity and are being affected by climate change, which was considered as one of the main threats to biodiversity conservation. However, the impacts of future climate change in shifted means and extremes of temperature, precipitation, and cloud cover under the representative concentration pathways (RCP 2.6, 4.5, and 8.5) on the characteristics of these ecoregions have yet to be fully understood. The present study was designed using a dynamic global vegetation model and both current and future climate scenarios, to investigate the impacts of shifted means and extremes of temperature, precipitation, and cloud cover on five ecological indicators including net primary productivity (NPP), carbon storage, runoff, wildfire risk, and habitat transformation at the ecoregional scale. The analysis was performed for the terrestrial ecoregions as a whole, as well as for specific subsets of CEs, VEs, and IEs. The results showed that future climate scenarios (whether RCP 2.6, 4.5, or 8.5) were estimated to increase the mean NPP, runoff, wildfire risk, and habitat transformation for all ecoregion types, when comparing values for 2071–2100 to the baseline (1971–2000) period. In contrast, the mean carbon storage in the TEWs, VEs, and CEs was estimated to decrease from the baseline to the values under RCP 2.6 and RCP 4.5 and then increase to their largest values under RCP 8.5. The mean carbon storage in the IEs under RCP 8.5 was estimated to remain lower than the baseline period values. Climate change in shifted means and extremes of temperature, precipitation, and cloud cover are generally significant drivers of the variances of NPP, carbon storage, runoff, wildfire risk, and habitat transformation under RCP 2.6, RCP 4.5, and RCP 8.5. The dynamics of the climate change metrics and the five ecological indicators have significant implications for biodiversity conservation in changing climates.

Snow Drought Risk and Susceptibility in the Western United States and Southwestern Canada

AbstractIn western North America (WNA), mountain snowpack supplies much of the water used for irrigation, municipal, and industrial uses. Thus, snow droughts (a lack of snow accumulation in winter) can have drastic ecological and socioeconomic impacts. In this study, the historical (1951–2013) frequency, severity, and risk (frequency × severity) of dry, warm, and warm and dry snow droughts are quantified at the grid‐cell and ecoregion scale for snow‐dominated regions in the western United States and southwestern Canada (sWNA). Based on multiple linear regression analysis, relationships between mean winter temperature, snow drought risk, and snow water equivalent sensitivity are explored. Piecewise linear regression is used to identify temperature thresholds for mapping temperature‐related snow drought susceptibility. Results highlight spatial differences in snow drought regimes across sWNA and reveal that temperature thresholds exist at −3.1 °C (±0.3 °C) and 1.4 °C (±0.3 °C), above which the warm snow drought risk increases more rapidly. Approximately 3% of the nonglaciated snow storage in this region has high susceptibility to temperature‐related snow drought, representing 11 km3 of water, or approximately one third the capacity of Lake Mead. Under a +2 °C climate scenario, an additional 8% (28 km3) of this snow storage volume will transition to high susceptibility.

Potential invasive plant expansion in global ecoregions under climate change.

Climate change is increasing the risk of invasive plant expansion worldwide. However, few studies have specified the relationship between invasive plant expansion and ecoregions at the global scale under climate change. To address this gap, we provide risk maps highlighting the response of invasive plant species (IPS), with a focus on terrestrial and freshwater ecoregions to climate change, and further explore the climatic features of ecosystems with a high potential for invasive plant expansion under climate change. We use species distribution modelling to predict the suitable habitats of IPS with records at the global scale. Hotspots with a potential risk of IPS (such as aquatic plants, trees, and herbs) expanding in global ecoregions were distributed in Northern Europe, the UK, South America, North America, southwest China, and New Zealand. Temperature changes were related to the potential of IPS expansion in global ecoregions under climate change. Coastal and high latitude ecoregions, such as temperate forests, alpine vegetation, and coastal rivers, were severely infiltrated by IPS under climate change. Monitoring strategies should be defined for climate change for IPS, particularly for aquatic plants, trees, and herbs in the biomes of regions with coastal or high latitudes. The role of climate change on the potential for IPS expansion should be taken into consideration for biological conservation and risk evaluation of IPS at ecoregional scales.

Macroecological and spatial patterns in the distribution of cladocerans in Alpine lakes

An extensive data set of the occurrence of 55 cladoceran species in 432 Alpine lakes was used to document patterns of species richness, assemblage composition and distribution across the Alpine ecoregion in Italy, determining the spatial and macroecological parameters explaining the observed patterns. Moreover, the influence of dispersal, environmental filtering and life cycle strategies in shaping cladoceran alpha and beta-diversity patterns was examined. Only three species where widely distributed in the Alpine lakes, with more than 80 % of the species present in less than 10 % of the lakes. Alpha-diversity ranged from 1 to 13 species, while beta-diversity was almost exclusively due to spatial turnover. Our data suggest that the excess of rarity in these environments was better explained by post-glacial random recolonization patterns, high dispersal ability, peculiar life strategies including the production of diapausing eggs, and compliance of cladocerans to the monopolization hypothesis. Spatial determinants (studied using Moran’s Eigenvector Maps) were important for large scale species distribution patterns, suggesting that dispersal was not a limiting factor, although altitudinal heterogeneity shaped beta-diversity patterns. The altitudinal gradient (together with the associated macroclimatic factors) was the second main determinant of species distribution, followed by the geological setting of the basins. Local factors played only a minor role in explaining cladoceran distribution patterns at the ecoregional scale.

Is ecoregional scale precise enough for lake nutrient criteria? Insights from a novel relationship-based clustering approach

While the ecoregional lake nutrient criteria have been widely used in the past two decades, the overconfidence on their applicability may mislead the pollution management decisions, considering the spatial heterogeneity within the ecoregion. The exploration of applicability is thereby important, but is hindered by the difficulty in recognizing reliable relationship patterns between the nutrient and management endpoint. We propose a novel relationship-based clustering approach (RCA) to explore whether the ecoregional scale is precise enough for nutrient criteria. The approach (a) simulates relationships using Bayesian Linear Models, (b) clusters lakes according to relationship similarities via relationship mapping and hierarchical clustering, and (c) identifies reliable relationship patterns based on the leave-one-out cross-validation. The RCA is then employed to explore Chlorophyll a-total phosphorus relationships of 34 lakes in four Ecological Drainage Units (EDUs) in the U.S. Long-term water quality data is from a newly established database (LAGOS-NE). The results show that multiple relationship patterns exist in all the EDUs. The ecoregional relationships misestimate the nutrient effect in over a half of lakes. Therefore, we determine that the ecoregional scale is not precise enough for nutrient criteria and the sub-ecoregional scale is then recommended. Besides, the RCA provides a backward thinking for determining the spatial scale and can be used in some other fields where relationship-based clustering is needed.

The importance of spatial scale in habitat selection by European beaver

We evaluated habitat selection by European beaver Castor fiber L. across a spatial gradient from local (within the family territory) to a broad, ecoregional scale. Based on aerial photography, we assessed the habitat composition of 150 beaver territories along the main water bodies of the Vistula River delta (northern Poland) and compared these data with 183 randomly selected sites not occupied by the species. The beavers preferred habitats with high availability of woody plants, including shrubs, and avoided anthropogenically modified habitats, such as arable lands. Within a single family territory, we observed decreasing woody plant cover with increasing distance from a colony centre, which suggests that beaver habitat preferences depend on the assessment of both the abundance and spatial distribution of preferred habitat elements. We tested the importance of spatial scale in beaver habitat selection with principal coordinates of neighbour matrices analysis, which showed that the geographical scale explained 46.7% of the variation in habitat composition, while the local beaver density explained only 10.3% of this variability. We found two main spatial gradients that were related to the broad spatial scale: first, the most important gradient was related to the largest distances between beaver sites and was independent of woody plant cover and the local beaver site density. The second most important gradient appeared more locally and was associated with these variables. Our results indicate that European beaver habitat selection was affected by different scale‐related phenomena related 1) to central place foraging behaviour, which resulted in the clumped distribution of woody plants within the territory, and 2) local population density and woody plant cover. Finally, 3) habitat selection occurs independently across the largest spatial scale studied (e.g. between watersheds), which was probably due to the limited natal dispersal range of the animals.

Gamma-diversity partitioning of gobiid fishes (Teleostei: Gobiidae) ensemble along of Eastern Tropical Pacific: Biological inventory, latitudinal variation and species turnover

Gobies are the most diverse marine fish family. Here, we analysed the gamma-diversity (γ-diversity) partitioning of gobiid fishes to evaluate the additive and multiplicative components of α and β-diversity, species replacement and species loss and gain, at four spatial scales: sample units, ecoregions, provinces and realms. The richness of gobies from the realm Eastern Tropical Pacific (ETP) is represented by 87 species. Along latitudinal and longitudinal gradients, we found that the γ-diversity is explained by the β-diversity at both spatial scales, ecoregions and provinces. At the ecoregion scale, species are diverse in the north (Cortezian ecoregion) and south (Panama Bight ecoregion) and between insular and coastal ecoregions. At the province scale, we found that the species turnover between the warm temperate Northeast Pacific (WTNP), Tropical East Pacific (TEaP) and the Galapagos Islands (Gala) was high, and the species nestedness was low. At the ecoregion scale, historical factors, and phylogenetic factors have influenced the hotspots of gobiid fish biodiversity, particularly in the Cortezian, Panama Bight and Cocos Island ecoregions, where species turnover is high across both latitudinal and longitudinal gradients. At the provincial level, we found that the contributions of the β-diversity from north to south, in the WTNP, TEaP and Gala were high, as result of the high number of unique species. Species turnover was also high at this scale, with a low contribution from species nestedness that was probably due to the low species/gene flow within the provinces. These results highlight the importance and successful inclusion of a cryptobenthic fish component in ecological and biogeographical studies.

Global patterns of interannual climate-fire relationships.

Climate shapes geographic and seasonal patterns in global fire activity by mediating vegetation composition, productivity, and desiccation in conjunction with land-use and anthropogenic factors. Yet, the degree to which climate variability affects interannual variability in burned area across Earth is less understood. Two decades of satellite-derived burned area records across forested and nonforested areas were used to examine global interannual climate-fire relationships at ecoregion scales. Measures of fuel aridity exhibited strong positive correlations with forested burned area, with weaker relationships in climatologically drier regions. By contrast, cumulative precipitation antecedent to the fire season exhibited positive correlations to nonforested burned area, with stronger relationships in climatologically drier regions. Climate variability explained roughly one-third of the interannual variability in burned area across global ecoregions. These results highlight the importance of climate variability in enabling fire activity globally, but also identify regions where anthropogenic and other influences may facilitate weaker relationships. Empirical fire modeling efforts can complement process-based global fire models to elucidate how fire activity is likely to change amidst complex interactions among climatic, vegetation, and human factors.

Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia).

High‐throughput sequencing of environmental DNA (i.e., eDNA metabarcoding) has become an increasingly popular method for monitoring aquatic biodiversity. At present, such analyses require target‐specific primers to amplify DNA barcodes from co‐occurring species, and this initial amplification can introduce biases. Understanding the performance of different primers is thus recommended prior to undertaking any metabarcoding initiative. While multiple software programs are available to evaluate metabarcoding primers, all programs have their own strengths and weaknesses. Therefore, a robust in silico workflow for the evaluation of metabarcoding primers will benefit from the use of multiple programs. Furthermore, geographic differences in species biodiversity are likely to influence the performance of metabarcoding primers and further complicate the evaluation process. Here, an in silico workflow is presented that can be used to evaluate the performance of metabarcoding primers on an ecoregion scale. This workflow was used to evaluate the performance of published and newly developed eDNA metabarcoding primers for the freshwater fish biodiversity of the Murray–Darling Basin (Australia). To validate the in silico workflow, a subset of the primers, including one newly designed primer pair, were used in metabarcoding analyses of an artificial DNA community and eDNA samples. The results show that the in silico workflow allows for a robust evaluation of metabarcoding primers and can reveal important trade‐offs that need to be considered when selecting the most suitable primer. Additionally, a new primer pair was described and validated that allows for more robust taxonomic assignments and is less influenced by primer biases compared to commonly used fish metabarcoding primers.

Identifying the critical riparian buffer zone with the strongest linkage between landscape characteristics and surface water quality

Influence of landscape pattern on water quality is complex and scale dependent. Existing literature mainly focuses on examining this influence at a wide range of spatial scales from local to basin level or to eco-regional scales. Studies on identifying the critical riparian zone, with certain buffer width and length that exhibit the strongest association between landscape characteristics and stream water quality, are still limited. Such identification is helpful for better understanding the influence of the adjacent landscape on stream water quality and is critical for effective landscape planning and local river management. In this study, the urban area of Xiangyang City along the Hanjiang River was selected as a case study. Water quality samples were collected at eight sites in the examined river system of the Hanjiang River from 2009 to 2014. Landscape pattern analysis, redundancy analysis and stepwise multiple linear regression analysis were used to explore the quantitative associations between landscape patterns and water quality. The results indicate that the landscape metrics that were selected explain approximately 63–87% of the variations in stream water quality at multiple buffer widths in 2009 and 2014. The strongest linkage between landscape characteristics and water quality occurred in the riparian zone with the buffer width of 300 m where the explanatory ability of the landscape metrics still varied at different buffer lengths and increased from 500 m to 8 km. Urban built-up land was more positively associated with degraded water quality at the smaller buffer widths than at the larger buffer widths, whereas forest land exhibited a stronger contribution to water quality improvements at wider buffer widths than at narrower buffer widths. The situation was different for the different buffer lengths. Urban built-up land was more correlated with water quality at longer buffer lengths, and forest land had a stronger contribution at shorter buffer lengths. Overall, landscape configuration seemed to have stronger effects on the water quality than landscape composition. These findings provide important information regarding multi-scale measures for sustainable landscape management to improve surface water quality.

Spatial patterns and environmental factors influencing leaf carbon content in the forests and shrublands of China

Leaf carbon content (LCC) is widely used as an important parameter in estimating ecosystem carbon (C) storage, as well as for investigating the adaptation strategies of vegetation to their environment at a large scale. In this study, we used a dataset collected from forests (5119 plots) and shrublands (2564 plots) in China, 2011–2015. The plots were sampled following a consistent protocol, and we used the data to explore the spatial patterns of LCC at three scales: plot scale, eco-region scale (n = 24), and eco-region scale (n = 8). The average LCC of forests and shrublands combined was 45.3%, with the LCC of forests (45.5%) being slightly higher than that of shrublands (44.9%). Forest LCC ranged from 40.2% to 51.2% throughout the 24 eco-regions, while that of shrublands ranged from 35% to 50.1%. Forest LCC decreased with increasing latitude and longitude, whereas shrubland LCC decreased with increasing latitude, but increased with increasing longitude. The LCC increased, to some extent, with increasing temperature and precipitation. These results demonstrate the spatial patterns of LCC in the forests and shrublands at different scales based on field-measured data, providing a reference (or standard) for estimating carbon storage in vegetation at a regional scale.

Human-related ignitions concurrent with high winds promote large wildfires across the USA

Large wildfires (>40 ha) account for the majority of burned area across the contiguous United States (US) and appropriate substantial suppression resources. A variety of environmental and social factors influence wildfire growth and whether a fire overcomes initial attack efforts and becomes a large wildfire. However, little is known about how these factors differ between lightning-caused and human-caused wildfires. This study examines differences in temperature, vapour pressure deficit, fuel moisture and wind speed for large and small lightning- and human-caused wildfires during the initial days of fire activity at ecoregion scales across the US. Large fires of both human and lightning origin occurred coincident with above-normal temperature and vapour pressure deficit and below-normal 100-hour dead fuel moisture compared with small fires. Large human-caused wildfires occurred, on average, coincident with higher wind speeds than small human-caused wildfires and large lightning-caused wildfires. These results suggest the importance of winds in driving rapid fire growth that can allow fires to overcome many of the factors that typically inhibit large human-caused fires. Additionally, such findings highlight the interplay between human activity and meteorological conditions and the importance of incorporating winds in modelling large-fire risk in human-dominated landscapes.

Aboveground biomass estimation at different scales for subtropical forests in China

BackgroundThe accurate estimation of forest biomass at different scales is the critical step in the assessment of forest carbon stocks. We used three models at increasing scales: allometric model at ecoregional scale (model 1), dummy variable allometric model at both ecoregion and regional scales (model 2), and allometric model at regional scale (model 3) to estimate the aboveground biomass of six subtropical forests in China. Furthermore, we also tested whether wood density can improve the accuracy of the allometric model at regional scale.ResultsAboveground biomass estimates for six subtropical forests were significantly affected by the ecoregions (p < 0.05). Model 1 and model 2 had good fitness with higher values of R2, lower RSE (residual standard error) and MPSE (mean percent standard error) than model 3. The values of MPSE for model 1, model 2, and model 3 ranged from 2.79 to 30.40%, 5.15 to 40.94%, and 13.25 to 80.81% at ecoregion scale, respectively. At regional scale, MPSE of model 2 was very similar to that of model 1, and was less than model 3. New allometric models with wood density had greater R2, lower RSE and MPSE than the traditional allometric models without wood density variable for six subtropical forests at regional scale.ConclusionThe dummy variable allometric models have better performances to estimate aboveground biomass for six subtropical forests in China, which provided an effective approach to improve the compatibility of forest biomass estimations from different scales. New allometric models with wood density substantially improved accuracies of aboveground biomass estimation for subtropical forests at regional scale.