Cover Metrics Research Articles

Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019)

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS (Moderate-resolution Imaging Spectroradiometer). We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and further subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences both in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a much shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and the other metrics is clearly related to elevation (r2 = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. As a result of high variability, no significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period.

Woody Vegetation Cover, Attrition, and Patch Metrics over Eight Decades in Central Texas, United States

Woody plant encroachment into rangeland ecosystems is a widespread and often unwelcomed circumstance affecting rangeland management decisions worldwide. In the rangeland management profession, varying philosophies have been employed in the management of woody plant encroachment. Following World War II, total eradication of woody plant cover was commonly practiced, eventually giving way to a mosaic approach that benefits livestock, wildlife, and recreational objectives, with cover increasing or even stabilizing in many areas. Cultural practices such as land fragmentation, lifestyles not dependent on agricultural income, and shifts in herbivory from predominately browsers to grazers may also be contributing factors. Modern image analysis technologies, such as object-oriented feature extraction and patch metric analyses, can shed light on past paradigm shifts through spectral and textural assessment of modern and historical aerial photography. In this study, woody plant cover and patch metrics were analyzed for a period spanning from 1938 to 1940 through 2018 in the Bennett and Sulphur Creek watersheds of the Lampasas Cut Plain of Central Texas. Object-based feature extraction was used to calculate woody plant cover, and Fragstats was used for landscape patch metrics. Total woody cover was compared with past stewardship paradigms. There was a net decrease of total woody plant cover from 1938 to 1940 through 2018, with variation in between as management paradigms shifted. A pattern of decline, regrowth, and stabilization, like that observed in other research, was noticed for the Bennett Creek watersheds but was not apparent in Sulfur Creek. Patch size/shape varied as well, but fractal patch complexity was relatively stable through time. Raster algebra analysis showed that < 10% of the initial woody cover from 1938 to 1940 remained in 2018, although total cover went through various expansion/reduction phases. This research underscores the importance of long-term datasets and locally based knowledge in the application and interpretation of historical management paradigms.

Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry

Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classes across three sites of ongoing restoration since linear disturbances in 2005, 2009, and 2014 in temperate and boreal coniferous forests in Alberta, Canada. The spatial arrangement of structure classes was assessed using land cover maps, mean patch size, and measures of local spatial association. We observed DAP heights of short-stature vegetation were consistently underestimated, but strong correlations (rs &gt; 0.75) with field height were found for juvenile trees, shrubs, and perennials. Metrics of height and canopy complexity allowed for the extraction of relatively tall and complex vegetation structures, whereas canopy cover and height variability metrics enabled the classification of the shortest vegetation structures. We found that the boreal site disturbed in 2009 had the highest cover of classes associated with complex vegetation structures. This included early regenerative (22%) and taller (13.2%) wood-like structures as well as structures representative of tall graminoid and perennial vegetation (15.3%), which also showed the highest patchiness. The developed tools provide large-scale maps of the structure, enabling the identification and assessment of vegetational patterns, which is challenging based on traditional field sampling that requires pre-defined location-based hypotheses. The approach can serve as a basis for the evaluation of specialized restoration objectives as well as objectives tailored towards processes of ecological succession, and support prioritization of future inspections and mitigation measures.

Determining maximum entropy in 3D remote sensing height distributions and using it to improve aboveground biomass modelling via stratification

McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural to describe a continuous variable (X) such as height. In this contribution we aim to provide a mathematical framework for determining maximum entropy in 3D remote sensing datasets based on the Gini Coefficient of theoretical continuous distributions, intended to replace FHD as entropy measure in vertical profiles of LiDAR heights (1D, X), with extensions to variables expressing dimensions of higher order (2D or 3D, Z ∝ X2 or X3). Then we apply this framework to Boreal forests in Finland to describe landscape heterogeneity with the intention to improve the modelling of forest aboveground biomass (AGB), hypothesizing that LiDAR models of AGB should essentially be different in areas of differing structural characteristics. We carried out a pre-stratification of LiDAR data collected in 2012 using simple rules applied to the L-skewness (Lskew) and L-coefficient of variation of LiDAR echo heights (Lcv; equivalent to the Gini coefficient, GCH), determining a new threshold at GCH = 0.33 as a consequence of the newly developed mathematical proofs. We observed only moderate improvements in terms of model accuracies: RMSDs reduced from 41.7% to 38.9 or 37.0%. More remarkably, we identified critical differences in the metrics selected at each stratum, which is useful to understand what predictor variables are more important for estimating AGB at each area of a forest. We observed that higher LiDAR height percentiles are more relevant at open canopies and heterogeneous forests, whereas closed canopies in homogeneous forests obtain most accurate predictions from a combination of cover metrics and percentiles around the median. Without stratification, the overall model would neglect explained variability in the structural types of lower occurrence, and predictions from a model influenced by structural types of higher occurrence would be biased at those areas. These results are thus useful in terms of improving our understanding on the relationships underlying LiDAR-AGB models.

Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies

Particulate matter with an aerodynamic equivalent dimeter less than 2.5 μm (PM2.5) and ozone (O3) are major air pollutants, with coupled and complex relationships. The control of both PM2.5 and O3 pollution requires the identification of their common influencing factors, which has rarely been attempted. In this study, land use regression (LUR) models based on the least absolute shrinkage and selection operator were developed to estimate PM2.5 and O3 concentrations in China's Pearl River Delta region during 2019. The common factors in the tradeoffs between the two air pollutants and their synergistic effects were analyzed. The model inputs included spatial coordinates, remote sensing observations, meteorological conditions, population density, road density, land cover, and landscape metrics. The LUR models performed well, capturing 54–89% and 42–83% of the variations in annual and seasonal PM2.5 and O3 concentrations, respectively, as shown by the 10-fold cross validation. The overlap of variables between the PM2.5 and O3 models indicated that longitude, aerosol optical depth, O3 column number density, tropospheric NO2 column number density, relative humidity, sunshine duration, population density, the percentage cover of forest, grass, impervious surfaces, and bare land, and perimeter-area fractal dimension had opposing effects on PM2.5 and O3. The tropospheric formaldehyde column number density, wind speed, road density, and area-weighted mean fractal dimension index had complementary effects on PM2.5 and O3 concentrations. This study has improved our understanding of the tradeoff and synergistic factors involved in PM2.5 and O3 pollution, and the results can be used to develop joint control policies for both pollutants.

Coral larval recruitment in north-western Australia predicted by regional and local conditions

Understanding ecological processes that shape contemporary and future communities facilitates knowledge-based environmental management. In marine ecosystems, one of the most important processes is the supply of new recruits into a population. Here, we investigated spatiotemporal variability in coral recruitment at 15 reefs throughout the Dampier Archipelago, north-western Australia between 2015 and 2017 and identified the best environmental predictors for coral recruitment patterns over this period. Large differences in recruitment were observed among years with the average density of recruits increasing by 375% from 0.017 recruits cm−2 in 2015 to 0.059 recruits cm−2 in 2017. Despite differences in recruitment among years, the rank order of coral recruit density among reefs remained similar among years, suggesting that spatial variation in recruitment within the Dampier Archipelago is partly deterministic and predictable. The density of coral recruits was best explained by percent cover of live corals at both local (within 5 m) and meso-scales (within 15 km), water turbidity and an oceanographic model that predicted larval dispersal. The highest density of coral recruits (~0.13 recruits cm−2 or 37 recruits per tile) occurred on reefs within sub-regions (15 km) with greater than 35% coral cover, low to moderate turbidity (KD490 < 0.2) and moderate to high modelled predictions of larval dispersal. Our results demonstrate that broad-scale larval dispersal models, when combined with local metrics of percent hard coral cover and water turbidity, can reliably predict the relative abundance of coral recruits over large geographical areas and thus can identify hotspots of recruit abundance and potential recovery following environmental disturbances; information that is essential for effective management of coral reefs.

Land Cover Effects on Selected Nutrient Compounds in Small Lowland Agricultural Catchments

The influence of landscape on nutrient dynamics in rivers constitutes an important research issue because of its significance with regard to water and land management. In the current study spatial and temporal variability of N-NO3 and P-PO4 concentrations and their landscape dependence was documented in the Świder River catchment in central Poland. From April 2019 to March 2020, water samples were collected from fourteen streams in the monthly timescale and the concentrations of N-NO3 and P-PO4 were correlated with land cover metrics based on the Corine Land Cover 2018 and Sentinel 2 Global Land Cover datasets. It was documented that agricultural lands and forests have a clear seasonal impact on N-NO3 concentrations, whereas the effect of meadows was weak and its direction was dependent on the dataset. The application of buffer zones metrics increased the correlation performance, whereas Euclidean distance scaling improved correlation mainly for forest datasets. The concentration of P-PO4 was not significantly related with land cover metrics, as their dynamics were driven mainly by hydrological conditions. The obtained results provided a new insight into landscape–water quality relationships in lowland agricultural landscape, with a special focus on evaluating the predictive performance of different land cover metrics and datasets.

Rapid coral reef assessment using 3D modelling and acoustics: acoustic indices correlate to fish abundance, diversity and environmental indicators in West Papua, Indonesia.

BackgroundProviding coral reef systems with the greatest chance of survival requires effective assessment and monitoring to guide management at a range of scales from community to government. The development of rapid monitoring approaches amenable to collection at community level, yet recognised by policymakers, remains a challenge. Technologies can increase the scope of data collection. Two promising visual and audio approaches are (i) 3D habitat models, generated through photogrammetry from video footage, providing assessment of coral cover structural metrics and (ii) audio, from which acoustic indices shown to correlate to vertebrate and invertebrate diversity, can be extracted.MethodsWe collected audio and video imagery using low cost underwater cameras (GoPro Hero7™) from 34 reef samples from West Papua (Indonesia). Using photogrammetry one camera was used to generate 3D models of 4 m2 reef, the other was used to estimate fish abundance and collect audio to generate acoustic indices. We investigated relationships between acoustic metrics, fish abundance/diversity/functional groups, live coral cover and reef structural metrics.ResultsGeneralized linear modelling identified significant but weak correlations between live coral cover and structural metrics extracted from 3D models and stronger relationships between live coral and fish abundance. Acoustic indices correlated to fish abundance, species richness and reef functional metrics associated with overfishing and algal control. Acoustic Evenness (1,200–11,000 Hz) and Root Mean Square RMS (100–1,200 Hz) were the best individual predictors overall suggesting traditional bioacoustic indices, providing information on sound energy and the variability in sound levels in specific frequency bands, can contribute to reef assessment.ConclusionAcoustics and 3D modelling contribute to low-cost, rapid reef assessment tools, amenable to community-level data collection, and generate information for coral reef management. Future work should explore whether 3D models of standardised transects and acoustic indices generated from low cost underwater cameras can replicate or support ‘gold standard’ reef assessment methodologies recognised by policy makers in marine management.

Impact of open pit coal mining on the forest landscape ecology using spatial metrics: a study of Barabani C.D. block, West Bengal

Mining is an extractive activity that creates pits, scars and waste dump especially when operated on open cast basis that not only degrades the landscape structure but also have significant impact over its functioning. There is a general consensus about the role of forest as an important entity of landscape structure in maintaining the ecological balance which is under immense pressure by the various anthropogenic activities like growth of built up area by means of urban industrial growth and expansion of mining area. This study has been conducted with an objective to assess the ecological health of the forest landscape and to analyse the land use land cover change in Barabini C.D. block of West Bengal, Eastern India where hectares of forest land has been greatly modified by open pit coal mining. The study has employed two satellite images of 2000 and 2017 to assess the land use land cover and spatial metrics of the study area. Modification of land use land cover to a large extent has been reported during the specified time, for instance, forest cover and agricultural land decreased considerably; built up area and mining area has been increased to a great extent. Landscape matrices of class level and landscape level concerning with the surface, shape, edge character and spatial distribution has been calculated to analyzed the spatio temporal character of the landscape. Increase in the landscape level spatial metrics value indicative of the development of a complex landscape structure with shrinking vegetative cover. The result of class level indices also reported that, the forest land not only fragmented by the formation of open cast pit but also experienced huge spatial loss over the specified time. This phenomenal change of the landscape structure is a matter of serious environmental concern that needs immediate attention for the sustainability of the region.

Understanding the Impacts of Land Cover Change on Water Quality in an Urbanized Stream

The heavily urbanized Johnson Creek watershed in the Portland, Oregon metropolitan region has experienced degraded water quality and historic mismanagement for decades. This paper reviews previous work on urbanized watersheds as well as studies that have explored Johnson Creek itself. Analysis of those previous studies is used to determine the present water quality within Johnson Creek and aims to predict how development and restoration will impact future water quality. While tracking water quality, stream temperature, total suspended solids (TSS), land cover metrics, extent of development, and canopy cover, this research expatiates on the effect of rising temperatures, the need for more responsive sediment transport, and increased spatial development within all portions of the watershed. Ultimately, this author has concluded that increased sunlight exposure through solar radiation loads on streams as a result of human-generated disturbances is a significant cause of stream temperature warming and the deterioration of water quality. Despite numerous and long-standing efforts for public-private cooperative stream restoration, the water quality in the Johnson Creek watershed continues to deteriorate.

Climate warming and the loss of sea ice: the impact of sea-ice variability on the southeastern Bering Sea pelagic ecosystem

AbstractWe investigated relationships among three metrics of sea-ice cover in eight regions of the eastern Bering Sea and the abundance of Calanus copepods, jellyfish medusae, and year-class strength of walleye pollock (Gadus chalcogrammus). In summer, Calanus spp. were more abundant over the middle shelf when sea ice lingered late into spring, and, to a lesser extent, when February sea-ice cover was heavy. Between 1982 and 1999, there were no significant (p ≤ 0.05) relationships between the amount or timing of sea-ice cover and pollock recruitment. However, between 2000 and 2015, pollock year-class strength was positively correlated with sea ice in the outer and middle shelves, with 17 of 24 regressions significant. Pollock year-class strength was best predicted by days with sea-ice cover after February. Pollock recruitment was positively influenced by copepod numbers, particularly in the middle shelf, with r2 values from 0.36 to 0.47. We hypothesize that the Calanus spp. present in the southeastern Bering Sea are primarily Calanus glacialis that have been advected south in association with sea ice. None of our sea-ice metrics explained the variance in jellyfish biomass. Jellyfish biomass in our study area in the pollock age-0 year was not correlated with pollock recruitment 3 years later.

Ground flora cover, diversity, and life‐history trait representation after wind disturbance, salvage logging, and prescribed fire in a Pinus palustris woodland

AbstractQuestionSalvage logging and prescribed fire are commonly applied in forests worldwide, yet little is known about the combined impacts of these management actions on early‐successional ecosystems. We questioned how operational‐scale prescribed fire would affect ground flora cover, species diversity, and life‐history trait expression in Pinus palustris woodlands differentially impacted by an Enhanced Fujita scale 3 (EF3) tornado and salvage logging.LocationFall Line Hills, Alabama, USA (32°55ʹ30ʺ N, 87°24ʹ00ʺ W).MethodsThe composition and foliar cover of woody and herbaceous plants ≤1 m height were monitored before and after prescribed fire in 600 1‐m2 quadrats distributed throughout mature, wind‐disturbed, and salvage‐logged Pinus palustris woodlands. Plant taxa were categorized by growth habit and life form to aid interpretation of multivariate analyses used to illustrate differences in ground flora assemblages between disturbance categories.ResultsPrescribed fire reduced ground flora cover, but not diversity, which remained greatest in wind‐disturbed sites that were not salvage‐logged. Though ground flora assemblages remained disparate between disturbance categories, prescribed fire imposed some consistent selective pressures on plants with common life‐history strategies. Post‐fire understorey tree cover reductions coincided with increased shrub cover throughout the treatment area. Nonetheless, differences in graminoid cover were exaggerated after prescribed fire, and representation of the geophyte life form was relatively constant across space and time.ConclusionsPrescribed fire did not counteract salvage‐mediated reductions in ground flora diversity. Retention of downed deadwood maximized early‐successional plant diversity in wind‐disturbed sites. Trait‐based ground flora analyses enabled a more detailed understanding of disturbance effects than aggregate metrics of foliar cover and diversity. In ecosystems with high floristic diversity, grouping plants by common life‐history traits can provide a simple and effective means to monitor impacts of management actions on ecosystem recovery.

Small Patches of Riparian Woody Vegetation Enhance Biodiversity of Invertebrates

Patches of riparian woody vegetation potentially help mitigate environmental impacts of agriculture and safeguard biodiversity. We investigated the effects of riparian forest on invertebrate diversity in coupled stream-riparian networks using a case study in the Zwalm river basin (Flanders, Belgium). Agriculture is one of the main pressures in the basin and riparian forest is limited to a number of isolated patches. Our 32 study sites comprised nine unshaded “unbuffered” sites which were paired with nine shaded “buffered” sites on the same stream reach, along with five ‘least-disturbed’ sites and nine downstream sites. We sampled water chemistry, habitat characteristics and stream and riparian invertebrates (carabid beetles and spiders) at each site. Three methods were used to quantify riparian attributes at different spatial scales: a visually-assessed qualitative index, quantitative estimates of habitat categories in six rectangular plots (10 × 5 m) and geographic information system (GIS)-derived land cover data. We investigated relationships between invertebrates and riparian attributes at different scales with linear regression and redundancy analyses. Spiders and carabids were most associated with local riparian attributes. In contrast, aquatic macroinvertebrates were strongly influenced by the extent of riparian vegetation in a riparian band upstream (100–300 m). These findings demonstrate the value of quantifying GIS-based metrics of riparian cover over larger spatial scales into assessments of the efficacy of riparian management as a complement to more detailed local scale riparian assessments in situ. Our findings highlight the value of even small patches of riparian vegetation in an otherwise extensively disturbed landscape in supporting biodiversity of both terrestrial and freshwater invertebrates and emphasize the need to consider multiple spatial scales in riparian management strategies which aim to mitigate human impacts on biodiversity in stream-riparian networks.

Everyone makes mistakes: Sampling errors in vegetation analysis - The effect of different sampling methods, abundance estimates, experimental manipulations, and data transformation

Understanding of causes for recent changes in vegetation structure and species richness of natural habitats is crucial for their maintaining for future generations. However, to avoid misinterpretation of vegetation changes in time, we should be aware of limits and errors of methods used for vegetation sampling. In a specific vegetation type, i.e. species rich wet meadow, we quantified sampling error in vegetation sampling at four different sampling levels (visual cover estimation, detailed recording in a grid of small cells, detailed assessment during clipping for biomass, biomass sorting), compared differences among three abundance estimates (frequency, cover and biomass), and assessed the effect of data transformation, and rapid change in vegetation structure caused by experimental species removal. At the 1 m2 scale the captured proportion of species missed by classical relevé sampling was on average 16%. Subsequent detailed subquadrat sampling captured the majority of previously overlooked species. The chance of a species being overlooked increased both with rarity, and the species richness of the area sampled. Where abundance was measured using metrics of cover and biomass, common species were overvalued, but when abundance was measured using frequency, common species were undervalued. In this study, logarithmic transformation of values provided a more reliable characterization of vegetation, than binarized or untransformed values. With the exception of species abundance, the number of species overlooked, quadrat species richness, and vegetation characterization were all affected by the experimental treatment. Our findings highlight the potential effects of error when conducting vegetation sampling and analyses of community dynamics. Due to these effects, we need to consider the reliability of conclusions drawn when assessing temporal changes in plant dynamics. Data transformation modifies the effect of sampling error in analyses of vegetation data.

Three decades of land-use and land-cover change in mountain regions of the Brazilian Atlantic Forest

Mountain regions are key hotspots for biodiversity conservation and for provisioning ecosystem services. Containing fragile ecosystems and home to millions of inhabitants, mountains are also places of great value for tourism, cultural practices and endemic species. In this paper, we developed the first multitemporal land-use and land-cover assessment of mountain regions within the Brazilian Atlantic Forest (BAF), the most endangered Brazilian tropical biome. The study used spatial thematic mapping for 1985, 2001, and 2018 to understand changes in landscape composition, patterns of change in patch metrics of natural vegetation cover, and correlations between human population and natural vegetation. Change detection techniques, landscape metrics and statistical tests (e.g., Kruskal-Wallis) were applied. We found that landscape composition did not change at significant levels over the 34 years (1985–2018), but that intense exchange between natural vegetation and agriculture creates a shifting mosaic steady-state. Additionally, natural vegetation loss was 13-fold lower within mountains than in other areas of the BAF biome, which indicates lower human-induced change in mountain regions. Urban and rural population (number of inhabitants) showed positive correlation with natural vegetation at municipality level indicating higher presence of population in municipalities with large extents of natural vegetation. Analysis demonstrated that the study region was under lower population pressure and urban growth compared to other areas and had kept large extents of natural vegetation within large patches, different to what is observed at biome level. However, telecoupling processes may result in indirect land changes in mountain regions of the BAF biome. Our results indicate that mountains play a key role in conserving the remnants of the BAF.

Multiple measures of monsoon-controlled water storage in Asia

Temporal water storage is a fundamental component of the terrestrial water cycle. Methods of estimating water storage variations are often limited to specific, well-monitored locations, and/or system scales. Thus, measures of storage from small systems can be difficult to compare to large systems. Here we compare three independent methods of estimating water storage variations for systems spanning over three orders of magnitude in basin area: 1) remote satellite observations (GRACE), 2) hydrograph recession curve analysis, and 3) quantifying precipitation-discharge hysteresis loops. We measured storage using all three methods for 242 watersheds in Asia (103 to 106 km2) and find that GRACE-derived storage correlates well with quantification of hysteresis terms but recession curve derived dynamic storage does not correlate with hysteresis terms or GRACE-derived storage. Thus, we argue that precipitation-discharge hysteresis may be able to be scaled to GRACE-derived storage as an independent estimate of storage for basins as small as 103 km2. Hysteresis-derived storage correlates well with mean monsoon rainfall in the upstream watershed while recession-derived dynamic storage does not. This suggests that hysteresis- and GRACE-derived storage may be input limited. In contrast, recession-derived dynamic storage does not correlate with topographic, climatic, or land cover metrics, suggesting that it may be limited by the rate at which water infiltrates into deep groundwater and then enters the river system. In addition, we find that recession-derived dynamic storage is a factor of seven lower than hysteresis-derived storage. We infer that hysteresis-derived storage includes recession curve-derived storage in addition to other storage units, such as snowpack, lakes, and soil moisture. Recession-derived dynamic storage in turn represents the annual variability in deep groundwater storage, a “leaky bucket” that is recharged from the top and “leaks” into rivers from deeper storage. These data may be able to be used to better quantify storage terms in hydrologic modeling.

Habitat heterogeneity captured by 30-m resolution satellite image texture predicts bird richness across the United States.

Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.

Patterns and drivers of the scale of effect of landscape structure on diurnal raptors in a fragmented tropical dry forest

Theoretical models propose that the spatial extent at which landscape structure best predicts species responses (scale of effect, SoE) depends on habitat and dietary specialization, landscape metrics, and response variables. However, empirical support for such models is scarce, especially for apex predators. To determine SoE for diurnal raptors, and test for differences among ecological traits of habitat and dietary specialization, landscape metrics, and response variables. We conducted 1.5 km transect surveys of diurnal raptors at 26 sites in a tropical dry forest from western Mexico. We measured four landscape metrics (forest cover, matrix hardness, forest patch density, edge density) in 16 concentric landscapes (400 to 3400 ha) around each survey site. We then assessed the landscape size at which each landscape metric best predicted each response variable (abundance, species richness, temporal beta diversity). Finally, we tested for differences in SoE among ecological traits of raptors, landscape metrics, and response variables. Landscape composition metrics of forest cover and matrix hardness decreased with increasing landscape size, and were the main landscape predictors influencing diurnal raptors. SoE differed significantly among landscape metrics, being larger for forest cover (2433.3 ha) than matrix hardness (1500 ha). However, SoE did not differ significantly among ecological traits of raptors, or response variables. SoE was mainly driven by spatial metrics, with diurnal raptors being more strongly associated with forest cover measured over larger scales. This supports findings for other taxa and suggests that forest cover influences dispersal success across larger scales.

Associations between Benthic Cover and Habitat Complexity Metrics Obtained from 3D Reconstruction of Coral Reefs at Different Resolutions

Quantifying the three-dimensional (3D) habitat structure of coral reefs is an important aspect of coral reef monitoring, as habitat architecture affects the abundance and diversity of reef organisms. Here, we used photogrammetric techniques to generate 3D reconstructions of coral reefs and examined relationships between benthic cover and various habitat metrics obtained at six different resolutions of raster cells, ranging from 1 to 32 cm. For metrics of 3D structural complexity, fractal dimension, which utilizes information on 3D surface areas obtained at different resolutions, and vector ruggedness measure (VRM) obtained at 1-, 2- or 4-cm resolution correlated well with benthic cover, with a relatively large amount of variability in these metrics being explained by the proportions of corals and crustose coralline algae. Curvature measures were, on the other hand, correlated with branching and mounding coral cover when obtained at 1-cm resolution, but the amount of variability explained by benthic cover was generally very low when obtained at all other resolutions. These results show that either fractal dimension or VRM obtained at 1-, 2- or 4-cm resolution, along with curvature obtained at 1-cm resolution, can effectively capture the 3D habitat structure provided by specific benthic organisms.