Spatio-temporal remotely sensed indices identify hotspots of biodiversity conservation concern

Over the course of a year, vegetation and temperature have strong phenological and seasonal patterns, respectively, and many species have adapted to these patterns. High inter-annual variability in the phenology of vegetation and in the seasonality of temperature pose a threat for biodiversity. However, areas with high spatial variability likely have higher ecological resilience where inter-annual variability is high, because spatial variability indicates presence of a range of resources, microclimatic refugia, and habitat conditions. The integration of inter-annual and spatial variability is thus important for biodiversity conservation. Areas where spatial variability is low and inter-annual variability is high are likely to limit resilience to disturbance. In contrast, areas of high spatial variability may be high priority candidates for protection. Our goal was to develop spatiotemporal remotely sensed indices to identify hotspots of biodiversity conservation concern. We generated indices that capture the inter-annual and spatial variability of vegetation greenness and land surface temperature and integrated them to identify areas of high, medium, and low biodiversity conservation concern. We applied our method in Argentina (2.8 million km2), a country with a wide range of climates and biomes. To generate the inter-annual variability indices, we analyzed MODIS Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) time series from 2001 to 2018, fitted curves to obtain annual phenological and seasonal metrics, and calculated their inter-annual variability. To generate the spatial variability indices, we calculated standard deviation image texture of Landsat 8 EVI and LST. When we integrated our inter-annual and spatial variability indices, areas in the northeast and parts of southern Argentina were the hotspots of highest conservation concern. High inter-annual variability poses a threat in these areas, because spatial variability is low. These are areas where management efforts could be valuable. In contrast, areas in the northwest and central-west are where protection should be strongly considered because the high spatial variability may confer resilience to disturbance, due to the variety of conditions and resources within close proximity. We developed remotely sensed indices to identify hotspots of high and low conservation concern at scales relevant to biodiversity conservation, = which can be used to target management actions in order to minimize biodiversity loss.

File: RSE_Silveira_2021.pdf

Contrasting seasonal patterns of relative temperature and thermal heterogeneity and their influence on breeding and winter bird richness patterns across the conterminous United States

Environmental heterogeneity enhances species richness by creating niches and providing refugia. Spatial variation in climate has a particularly strong positive correlation with richness, but is often indirectly inferred from proxy variables, such as elevation and related topographic heterogeneity indices, or derived from interpolated coarsegrain weather station data. Our aim was to develop new remotely sensed metrics of relative temperature and thermal heterogeneity, compare them with proxy measures, and evaluate their performance in predicting species richness patterns. We analyzed Landsat 8’s Thermal Infrared Sensor data, calculated two thermal metrics during summer and winter, and compared their seasonal spatial patterns with those of elevation and topographic heterogeneity. We fit generalized least squares models to evaluate each variable’s effect in predicting seasonal bird richness using data from the North American Breeding Bird Survey. Generally speaking, neither elevation nor topographic heterogeneity were good proxies for temperature or thermal heterogeneity, respectively. Relative temperature had a non-linear relationship with elevation that was negatively quadratic in summer, but slightly positively quadratic in winter. Topographic heterogeneity had a stronger positive relationship with thermal heterogeneity in winter than in summer. The magnitude and direction of elevation–temperature and topographic heterogeneity–thermal heterogeneity relationships in each season also varied substantially across ecoregions. Remotely sensed metrics of relative temperature and thermal heterogeneity improved the predictive performance of species richness models, and both thermal variables had significant effects on bird richness that were independent of elevation and topographic heterogeneity. Thermal heterogeneity was positively related to total breeding bird richness, migrant breeding bird richness and resident bird richness, whereas topographic heterogeneity was negatively related to total breeding richness and unrelated to migrant or resident bird richness. Because thermal and topographic heterogeneity had contrasting seasonal patterns and effects on richness, they must be carefully contextualized when guiding conservation priorities.

File: ecog.05520.pdf

Developing the Dynamic Habitat Indices from Landsat satellite data to assess bird richness and bird abundance in conterminous U.S.

The DHIs calculated from 30-m Landsat for the conterminous US. The DHIs are shown as variation DHI in red, cumulative DHI in green, and minimum DHI in blue.

Human activity cause major changes to the planet and biodiversity is declining at an alarming rates. In order to prevent biodiversity loss, conservation actions require to assess current status of biodiversity to better understand and predict future changes, to identify the major drivers of biodiversity patterns, and to map biodiversity patterns. However, monitoring biodiversity over large areas is challenging to do in the field. Remote sensing provides the opportunity to develop indices that are designed for biodiversity assessment, because satellite data are collected systematically across broad scales. Vegetation productivity is one of the important determinants of species richness and density across broad scale. Vegetation indices derived from satellite data are a good proxy for vegetation productivity over broad areas. The Dynamic Habitat Indices (DHIs) summarize the three different aspects of vegetation productivity: cumulative productivity, minimum productivity, and seasonality in the way that it became relevant for biodiversity (Hobi et al., 2017; Radeloff et al., 2019; Razenkova et al., 2020). However, so far the DHIs have only been derived from coarse-resolution satellite imagery, which limits their value for management decisions.

The DHIs based on Landsat 8 in Colorado US, spatial resolution 30-m.
The DHIs based on Landsat 8 in Colorado US, spatial resolution 30-m.
The DHIs based on MODIS in Colorado US, spatial resolution 1-km.
The DHIs based on MODIS in Colorado US, spatial resolution 1-km.

Our goal is to develop the DHIs using medium-resolution Landsat imagery for monitoring biodiversity and abundance pattern across the conterminous United States. The main advantage is that imagery with medium resolution provides more detailed information about the spatial patterns of productivity. Our rationale was that the DHIs with higher spatial resolution could capture the difference in vertical structure of vegetation and characterize habitat heterogeneity at much finer scale, especially in complex mountainous terrain and areas with fragmented land cover. Having this crucial information in my hand, will help to understand how species respond to anthropogenic modification of landscapes, which disturb the integrity of landscape pattern. However, the temporal resolution of Landsat is low, and that creates a lot of challenges for the calculation of the DHIs.

We will develop the DHIs for the conterminous United States and test the usefulness of the DHIs for explaining the avian species richness and abundance pattern. Our study covers a wide range of ecoregions, and has diverse climatic zones and topography, resulting in a large number of habitats and large ranges of the DHIs. Moreover, rich datasets for bird richness and abundance are available for the US, particularly the western US. Our research will add more understanding to the importance of higher spatial resolution for characterizing the DHIs metrics and consequently for modeling biodiversity and individual species pattern. Moreover, our work will add more knowledge about drivers of avian diversity across broad spatial extents that can be used to predict how biodiversity patterns will change in the future depending on changes in vegetation productivity.

Deriving the Winter Habitat Indices (WHIs) from satellite data for biodiversity and conservation applications around the world

Given the rapid rate of climatic change occurring during the winter months, particularly in the Northern Hemisphere, researchers are working diligently to assess the potential effects of these changes on biodiversity assessments and conservation planning. A major hindrance to these efforts to date has been a lack of remotely sensed indices that characterize winter conditions for ecological questions across large spatiotemporal scales. David and Likai are addressing this need by leveraging the wealth of available satellite data to derive new indices of snow and frozen ground dynamics. These Winter Habitat Indices (WHIs) capture several biologically important aspects of winter, including overall length, within-season climate variability, and potential subnivium conditions.

(1) Winter Habitat Indices (WHIs) for the contiguous US derived from MODIS data (500-m) by David Gudex-Cross. Calculated annually from 2000/01 – 2018/19. These indices summarize satellite observations of winter conditions in ecologically meaningful ways.

To date, David has developed three WHIS for the contiguous US using MODIS snow observations and temperature data from Daymet at 500-meter spatial resolution: snow season length, snow cover variability, and the frequency of frozen ground without snow days. First, snow season length is the total number of days between the first and last snow in a given year (a measure of overall winter length). Second, snow cover variability captures the frequency with which a pixel is snow covered then not (ablation) or vice versa (new snow) within a given snow season. This index is especially important for species that rely on white coloration during the winter for camouflage, such as snowshoe hares, because it enables the identification of potential mismatch periods (i.e., when an individual is in its white color morph but there is no snow). Third, the frequency of frozen ground without snow days approximates subnivium conditions, with higher frequencies meaning organisms faced harmful freezing conditions without the thermal refugia provided by snow more often in a given year.

Using MODIS snow observations and freeze/thaw data from microwave sensors, Likai previously developed three WHIs globally at a 25-kilometer spatial resolution: duration of frozen ground, frozen ground with snow, and frozen ground without snow. Now he has also derived the snow cover variability index mentioned across the globe. The duration of frozen ground is arguably one of the most biologically appropriate definitions of winter length, especially for vegetation. The duration of frozen ground with and without snow indices are again approximating subnivium conditions, which provide critical insulation against freezing temperatures for soil organisms, plants, and animals.

(2) Global Winter Habitat Indices derived from satellite data (25-km) by Likai Zhu: frozen season length (top-left), duration of frozen ground without (bottom-left) and with (bottom-right) snow, and snow cover variability (top-right). Calculated annually from 2000/01 – 2018/19.

To assess the potential of the WHIs for ecological research and predicting biodiversity patterns, David is working with collaborators at UW (Ben Zuckerberg, Jon Pauli, and Spencer Keyser) and the Cornell Lab of Ornithology (Daniel Fink) to model relationships between the WHIs and species richness for birds estimated from eBird data. The results have been promising: all of the WHIs have shown strong relationships with species richness patterns for birds across the US. Expectedly, measures of winter length (i.e., snow season length and duration of frozen ground) have a negative relationship with species richness across all taxa: areas with long winters have fewer species relative to those with shorter winters. The snow cover variability and frozen ground without snow indices, on the other hand, have more complex, nonlinear relationships with bird species richness. Generally, snow cover variability has had a positive relationship with species richness: areas with higher snow cover variability have more bird species than those with lower variability.

These results highlight the great potential and promise of the WHIs for ecological research, biodiversity assessments, and conservation planning. David and Likai continue to derive the WHIs from new datasets in hopes of maximizing the spatial and temporal resolutions available to researchers, with the latest being the 30-meter harmonized Landsat 8-Sentinel 2 dataset. Given the rapid rate of winter climate change, they hope others will utilize the WHIs in their winter ecology studies and species distribution models to inform conservation strategies moving forward.

Winter Habitat Indices (WHIs) for the contiguous US and their relationship with winter bird diversity

The seasonal dynamics of snow cover strongly affect ecosystem processes and winter habitat, making them an important driver of terrestrial biodiversity patterns. Snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites can capture these dynamics over large spatiotemporal scales, allowing for the development of indices with specific application in ecological research and predicting biodiversity. Here, our primary objective was to derive winter habitat indices (WHIs) from MODIS that quantify snow season length, snow cover variability, and the prevalence of frozen ground without snow as a proxy for subnivium conditions. We calculated the WHIs for the full snow year (Aug-Jul) and winter months (Dec-Feb) across the contiguous US from 2003/04 to 2017/18 and validated them with ground-based data from 797 meteorological stations. To demonstrate the potential of the WHIs for biodiversity assessments, we modeled their relationships with winter bird species richness derived from eBird observations. The WHIs had clear spatial patterns reflecting both altitudinal and latitudinal gradients in snow cover. Snow season length was generally longer at higher latitudes and elevations, while snow cover variability and frozen ground without snow were highest across low elevations of the mid latitudes. Variability in the WHIs was largely driven by elevation in the West and by latitude in the East. Snow season length and frozen ground without snow were most accurately mapped, and had correlations with station data across all years of 0.91 and 0.85, respectively. Snow cover variability was accurately mapped for winter (r = 0.79), but not for the full snow year (r = 􀀀 0.21). The model containing all three WHIs used to predict winter bird species richness patterns across the contiguous US was by far the best, demonstrating the individual value of each index. Regions with longer snow seasons generally supported fewer species. Species richness increased steadily up to moderate levels of snow cover variability and frozen ground without snow, after which it steeply declined. Our results show that the MODIS WHIs accurately characterized unique gradients of snow cover dynamics and provided important information on winter habitat conditions for birds, highlighting their potential for ecological research and conservation planning.

File: GudexCross_etal_2021_MODIS_WHIs_BirdDiversity.pdf

Satellite image texture captures vegetation heterogeneity and explains patterns of bird richness

Addressing global declines in biodiversity requires accurate assessments of key environmental attributes determining patterns of species diversity. Spatial heterogeneity of vegetation strongly affects species diversity patterns, and measures of vegetation structure derived from lidar and satellite image texture analysis correlate well with species richness. Our goal here was to gain a better understanding of why image texture explains bird richness, by linking field-based measures of vegetation structure directly with both image texture and bird richness. In addition, we asked how image texture compares with lidar-based canopy height variability, and how sensor resolution affects the explanatory power of image texture. We generated texture metrics from 30 m (Landsat 8) and 10 m (Sentinel-2) resolution Enhanced Vegetation Index (EVI) imagery from 2017 to 2019. We compared textures with vegetation metrics and bird richness data from 27 National Ecological Observatory Network (NEON) terrestrial field sites across the continental US. Both 30 and 10 m resolution texture metrics were strongly correlated with lidar-based canopy height variability (|r| = 0.64 and 0.80, respectively). Texture was moderately correlated with field-based metrics, including variability of vegetation height and tree stem diameter, and foliage height diversity (range |r| = 0.31–0.52). Generally, 10 m resolution texture had stronger correlations with lidar and field-based metrics than 30 m resolution texture. In univariate linear models of total bird richness, 10 m resolution texture metrics also had higher explanatory power (up to R2adj = 0.45), than 30 m texture metrics (up to R2adj = 0.31). Among all metrics evaluated, the 10 m homogeneity texture was the best univariate predictor of total bird richness. In multivariate bird richness models that combined texture with lidar-based canopy height variability and field-based metrics, both 30 m and 10 m resolution texture metrics were selected in top-ranked models and independently contributed explanatory power (up to R2adj = 46%). Lidar-based canopy height variability was also selected in a top-ranked model of total bird richness, but independently contributed only 15% of the variance explained. Our results show satellite image texture characterized multiple features of structural and compositional vegetation heterogeneity, complemented more commonly used metrics in models of bird richness and for some guilds outperformed both lidar-based canopy height variability and field-based vegetation measurements. Ours is the first study to directly link image texture both to specific components of vegetation heterogeneity and to bird richness across multiple ecoregions and spatial resolutions, thereby shedding light on habitat features underlying the strong correlation between image texture and biodiversity.

File: Farwell-et-al-2021_Sat-image-texture_veg_birds_RemSensEnv.pdf

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.

File: Farwell-eta-l-2020_Habitat-heterogeneity-30-m-and-birds_Ecological-Applications.pdf

Restoring riparian forests according to existing regulations could greatly improve connectivity for forest fauna in Chile

Habitat connectivity is essential to facilitate species movement across fragmented landscapes, but hard to achieve at broad scales. The enforcement of existing land use policies could improve habitat connectivity, while providing legal support for implementation. Our goal was to evaluate how forest connectivity is affected if forests are restored according to existing riparian buffer regulations in Chile. We simulated forest restoration within 30 and 200 m of rivers in 99 large watersheds, following two sections of the forest regulation. We mapped habitat for two model forest species that have different minimum habitat sizes (15 and 30 ha), and for each we identified forest habitats and corridors using image morphology analysis. To quantify change in connectivity, we used a network graph index, the Relative Equivalent Connected Area. We found that both 30- and 200-m riparian buffers could have a positive effect on habitat connectivity. The 200-m buffers increased connectivity the most where forest cover was 20–40% (40% mean increase in connectivity index), while the 30-m buffers increased connectivity the most where forest cover was 40–60% (30% mean increase in connectivity index). The effect of riparian restoration scenarios was similar for both model species, suggesting that effective implementation of existing forest regulation could improve connectivity for fauna with a range of minimum habitat size requirements. Our findings also suggest that there is some flexibility in the buffer sizes that, if restored, would increase habitat connectivity. This flexibility could help ease the social and economic cost of implementing habitat restoration in productive lands.

File: Rojas_etal_2020_riparian_restoration_connectivity_chile_Land_urb_plan.pdf

Conservation planning for island nations: Using a network analysis model to find novel opportunities for landscape connectivity in Puerto Rico

Oceanic islands are important habitats for many endemic species. Global conservation assessments, however, are too coarse to characterize areas of high human influence or landscape connectivity at a resolution that is useful for conservation planning on most islands. Our goal was to identify landscape elements that are essential for the maintenance of structural connectivity among natural habitat patches on islands. Using the Caribbean island of Puerto Rico as a case study, our specific objectives were to: (1) develop a map of the human footprint, and (2) characterize the connectivity of patches exhibiting low human modification that structurally connect the island’s ecological network. We used the human footprint as a measure of impediments to connectivity among Puerto Rico’s natural areas using network analysis. We found that more than half of Puerto Rico’s current land surface had a low human footprint (56%), but that coastal areas were highly affected by human use (82%). Puerto Rico possesses a compact network of natural areas, with a few patches in the interior mountains critical to structural connectivity. The number of isolated patches is very high; more than 60% of the patches were 2000 m or more apart. Identifying sites that are key hubs to connectivity on islands and ensuring they remain undeveloped is one strategy to balance land use and conservation, and to facilitate the persistence of endemic species. We show here how to improve general conservation assessment methods to be more relevant for islands. There is potential to support an interconnected network of natural areas that promotes landscape connectivity in Puerto Rico among noncoastal habitats, because the human activities are concentrated along the coast whereas the interior mountain range has a relatively low human footprint.

File: Guzman-Colon-et-al_2020_Conservation-planning-for-island-nations.pdf

Potential adaptability of marine turtles to climate change may be hindered by coastal development in the USA

Marine turtles may respond to projected climatic changes by shifting their nesting range to climatically suitable areas, which may
result in either increased exposure to threats or fewer threats. Therefore, there is the need to identify whether habitat predicted to
be climatically suitable for marine turtle nesting in the future will be affected by future threats and hinder marine turtles’ ability to
adapt. We modelled the geographic distribution of climatically suitable nesting habitat for marine turtles in the USA under future
climate scenarios, identified potential range shifts by 2050, determined impacts from sea-level rise, and explored changes in
exposure to coastal development as a result of range shifts. Overall nesting ranges of marine turtle species were not predicted to
change between the current and future time periods, except for the northern nesting boundaries for loggerhead turtles. However,
declines in climatically suitable nesting grounds were predicted; loggerhead turtles will experience the highest decreases (10%) in
climatically suitable habitat followed by green (7%) and leatherback (1%) turtles. However, sea-level rise is projected to inundate
78–81% of current habitat predicted to be climatically suitable in the future, depending on species and scenario. Nevertheless,
new beaches will also form, and suitable nesting habitat could be gained, with leatherback turtles potentially experiencing the
biggest percentage gain in suitable habitat.

File: 2020_Fuentes_et_al-2020-Regional_Environmental_Change.pdf