High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands.

Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki (Oreomystis bairdi) and 'akeke'e (Loxops caeruleirostris), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km2 for 'akikiki, compared to the current Kaua'i range of 13.09 km2 . In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km2 , respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'akohekohe (Palmeria dolei), Maui 'alauahio (Paroreomyza montana), and kiwikiu (Pseudonestor xanthophrys). Weighted overlap areas between the species from both islands were moderate (<12 km2 ), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.

A Near Four-Decade Time Series Shows the Hawaiian Islands Have Been Browning Since the 1980s.

The Hawaiian Islands have been identified as a global biodiversity hotspot. We examine the Normalized Difference Vegetation Index (NDVI) using Climate Data Records products (0.05 × 0.05°) to identify significant differences in NDVI between neutral El Niño-Southern Oscillation years (1984, 2019) and significant long-term changes over the entire time series (1982-2019) for the Hawaiian Islands and six land cover classes. Overall, there has been a significant decline in NDVI (i.e., browning) across the Hawaiian Islands from 1982 to 2019 with the islands of Lana'i and Hawai'i experiencing the greatest decreases in NDVI (≥44%). All land cover classes significantly decreased in NDVI for most months, especially during the wet season month of March. Native vegetation cover across all islands also experienced significant declines in NDVI, with the leeward, southwestern side of the island of Hawai'i experiencing the greatest declines. The long-term trends in the annual total precipitation and annual mean Palmer Drought Severity Index (PDSI) for 1982-2019 on the Hawaiian Islands show significant concurrent declines. Primarily positive correlations between the native ecosystem NDVI and precipitation imply that significant decreases in precipitation may exacerbate the decrease in NDVI of native ecosystems. NDVI-PDSI correlations were primarily negative on the windward side of the islands and positive on the leeward sides, suggesting a higher sensitivity to drought for leeward native ecosystems. Multi-decadal time series and spatially explicit data for native landscapes provide natural resource managers with long-term trends and monthly changes associated with vegetation health and stability.

Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing.

There is an increasing interest in identifying theories, empirical data sets, and remote-sensing metrics that can quantify tropical forest alpha diversity at a landscape scale. Quantifying patterns of tree species richness in the field is time consuming, especially in regions with over 100 tree species/ha. We examine species richness in a 50-ha plot in Barro Colorado Island in Panama and test if biophysical measurements of canopy reflectance from high-resolution satellite imagery and detailed vertical forest structure and topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (>1, 1-10, >10, and >20 cm dbh) and three spatial scales (1, 0.25, and 0.04 ha). We use the 2010 tree inventory, including 204,757 individuals belonging to 301 species of freestanding woody plants or 166 ± 1.5 species/ha (mean ± SE), to compare with remote-sensing data. All remote-sensing metrics became less correlated with species richness as spatial resolution decreased from 1.0 ha to 0.04 ha and tree size increased from 1 cm to 20 cm dbh. When all stems with dbh > 1 cm in 1-ha plots were compared to remote-sensing metrics, standard deviation in canopy reflectance explained 13% of the variance in species richness. The standard deviations of canopy height and the topographic wetness index (TWI) derived from lidar were the best metrics to explain the spatial variance in species richness (15% and 24%, respectively). Using multiple regression models, we made predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predicted variation in tree species richness among all plants (adjusted r² = 0.35) and trees with dbh > 10 cm (adjusted r² = 0.25). However, the best model results were for understory trees and shrubs (dbh 1-10 cm) (adjusted r² = 0.52) that comprise the majority of species richness in tropical forests. Our results indicate that high-resolution remote sensing can predict a large percentage of variance in species richness and potentially provide a framework to map and predict alpha diversity among trees in diverse tropical forests.

Spatial conservation planning framework for assessing conservation opportunities in the Atlantic Forest of Brazil.

Historic rates of habitat change and growing exploitation of natural resources threaten avian biodiversity in the Brazilian Atlantic Forest, a global biodiversity hotspot. We implemented a twostage framework for conservation planning in the Atlantic Forest. First, we used ecological niche modeling to predict the distributions of 23 endemic bird species using 19 climatic metrics and 12 spectral and radar remote sensing metrics. Second, we utilized the principle of complementarity to prioritize new sites to augment the Atlantic Forest's existing reserves. The best predictors of bird distributions were precipitation metrics (the seasonality of rainfall) and radar remote sensing metrics (QSCAT). The existing protected areas do not include 10% of the habitat of each of the 23 endemic species. We propose a more economical set of protected areas by reducing the extent to which new sites duplicate the biodiversity content of existing protected areas. There is a high concordance between the proposed conservation areas that we designed using computerized algorithms and Important Bird Areas prioritized by BirdLife International. Insofar as deforestation in the Atlantic Forest is similar to land conversion in other biodiversity hotspots, our methodology is applicable to conservation efforts elsewhere in the world.

The Spectral Species Concept in Living Color.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Comparison of agroforests and protected forests in the East Usambara mountains, Tanzania.

Comparative studies on plant species richness, endemism, floristic composition, and structure between protected and unprotected forests are few in the Eastern Arc Mountains, one of the most biodiverse ecosystems in Africa. This study from one mountain range, the East Usambaras, examines floristic and structural tree data from 41-0.5 ha plots in four types of Eastern Arc forest: active agroforests, recently abandoned agroforests, mature secondary forest, and natural forest. Active agroforests had significantly lower tree species richness, endemic species richness, and stand density compared to natural and mature secondary forest. Recently abandoned agroforests contained a higher tree species richness, density, and tree height than active agroforests. Active and abandoned agroforests were dominated by an invasive tree, Maesopsis eminii. This tree species makes up a large percentage of the stems in active agroforests (26%), recently abandoned agroforests (32%), and in the canopy of mature secondary forests ∼ 30 years post logging (30%). Through time the increasing dominance of this non-native tree in active agroforests is a concern when considering the role of agroforests in a landscape scale conservation strategy.

OpenNahele: the open Hawaiian forest plot database.

BACKGROUND: This data paper provides a description of OpenNahele, the open Hawaiian forest plot database. OpenNahele includes 530 forest plots across the Hawaiian archipelago containing 43,590 individuals of 185 native and alien tree, shrub and tree fern species across six islands. We include estimates of maximum plant size (D950.1 and Dmax3) for 58 woody plant species, a key functional trait associated with dispersal distance and competition for light. OpenNahele can serve as a platform to test key ecological, evolutionary and conservation questions in a hotspot archipelago. NEW INFORMATION: OpenNahele is the first database that compiles data from a large number of forest plots across the Hawaiian archipelago to allow broad and high resolution studies of biodiversity patterns.Keywords: Hawaii, forests, islands, biodiversity, community ecology, evolutionary ecology.

Assessment and Prediction of Natural Hazards from Satellite Imagery.

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earth's surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earth's surface. High-resolution passive sensors have been used to successfully assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily ≤ 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space.

Nighttime lights time series of tsunami damage, recovery, and economic metrics in Sumatra, Indonesia.

On 26 December 2004, a magnitude 9.2 earthquake off the west coast of the northern Sumatra, Indonesia resulted in 160,000 Indonesians killed. We examine the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light imagery brightness values for 307 communities in the Study of the Tsunami Aftermath and Recovery (STAR), a household survey in Sumatra from 2004 to 2008. We examined night light time series between the annual brightness and extent of damage, economic metrics collected from STAR households and aggregated to the community level. There were significant changes in brightness values from 2004 to 2008 with a significant drop in brightness values in 2005 due to the tsunami and pre-tsunami nighttime light values returning in 2006 for all damage zones. There were significant relationships between the nighttime imagery brightness and per capita expenditures, and spending on energy and on food. Results suggest that Defense Meteorological Satellite Program nighttime light imagery can be used to capture the impacts and recovery from the tsunami and other natural disasters and estimate time series economic metrics at the community level in developing countries.

Asynchronous response of tropical forest leaf phenology to seasonal and el Niño-driven drought.

The Hawaiian Islands are an ideal location to study the response of tropical forests to climate variability because of their extreme isolation in the middle of the Pacific, which makes them especially sensitive to El Niño-Southern Oscillation (ENSO). Most research examining the response of tropical forests to drought or El Niño have focused on rainforests, however, tropical dry forests cover a large area of the tropics and may respond very differently than rainforests. We use satellite-derived Normalized Difference Vegetation Index (NDVI) from February 2000-February 2009 to show that rainforests and dry forests in the Hawaiian Islands exhibit asynchronous responses in leaf phenology to seasonal and El Niño-driven drought. Dry forest NDVI was more tightly coupled with precipitation compared to rainforest NDVI. Rainforest cloud frequency was negatively correlated with the degree of asynchronicity (Delta(NDVI)) between forest types, most strongly at a 1-month lag. Rainforest green-up and dry forest brown-down was particularly apparent during the 2002-003 El Niño. The spatial pattern of NDVI response to the NINO 3.4 Sea Surface Temperature (SST) index during 2002-2003 showed that the leeward side exhibited significant negative correlations to increased SSTs, whereas the windward side exhibited significant positive correlations to increased SSTs, most evident at an 8 to 9-month lag. This study demonstrates that different tropical forest types exhibit asynchronous responses to seasonal and El Niño-driven drought, and suggests that mechanisms controlling dry forest leaf phenology are related to water-limitation, whereas rainforests are more light-limited.

Use of remote sensing techniques to determine the effects of grazing on vegetation cover and dune elevation at Assateague Island National Seashore: impact of horses.

The effects of grazing by feral horses on vegetation and dune topography at Assateague Island National Seashore were investigated using color-infrared imagery, lidar surveys, and field measurements. Five pairs of fenced and unfenced plots (300 m2) established in 1993 on sand flats and small dunes with similar elevation, topography, and vegetation cover were used for this study. Color-infrared imagery from 1998 and field measurements from 2001 indicated that there was a significant difference in vegetation cover between the fenced and unfenced plot-pairs over the study period. Fenced plots contained a higher percentage of vegetation cover that was dominated by American beachgrass (Ammophila breviligulata). Lidar surveys from 1997, 1999, and 2000 showed that there were significant differences in elevation and topography between fenced and unfenced plot-pairs. Fenced plots were, on average, 0.63 m higher than unfenced plots, whereas unfenced plots had generally decreased in elevation after establishment in 1993. Results demonstrate that feral horse grazing has had a significant impact on dune formation and has contributed to the erosion of dunes at Assateague Island. The findings suggest that unless the size of the feral horse population is reduced, grazing will continue to foster unnaturally high rates of dune erosion into the future. In order to maintain the natural processes that historically occurred on barrier islands, much larger fenced exclosures would be required to prevent horse grazing.