Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available
energy supporting higher species richness. A good indicator for available energy is Gross Primary Productivity
(GPP), which can be estimated from satellite data.
Our question was how temporal dynamics in GPP affect bird species richness. Specifically, we evaluated the
potential of the Dynamic Habitat Indices (DHIs) derived from MODIS GPP data together with environmental and
climatic variables to explain annual patterns in bird richness across the conterminous United States. By focusing
on annual DHIs, we expand on previous applications of multi-year composite DHIs, and could evaluate lag-effects
between changes in GPP and species richness.
We used 8-day GPP data from 2003 to 2013 to calculate annual DHIs, which capture three aspects of vegetation
productivity: (1) annual cumulative productivity, (2) annual minimum productivity, and (3) annual
seasonality expressed as the coefficient of variation in productivity. For each year from 2003 to 2013, we
calculated total bird species richness and richness within six functional guilds, based on North American
Breeding Bird Survey data.
The DHIs alone explained up to 53% of the variation in annual bird richness within the different guilds
(adjusted deviance-squared D2adj = 0.20–0.52), and up to 75% of the variation (D2adj = 0.28–0.75) when
combined with other environmental and climatic variables. Annual DHIs had the highest explanatory power for
habitat-based guilds, such as grassland (D2adj = 0.67) and woodland breeding species (D2adj = 0.75). We found
some inter-annual variability in the explanatory power of annual DHIs, with a difference of 5–7 percentage
points in explained variation among years in DHI-only models, and 3–7 points for models combining DHI,
environmental and climatic variables. Our results using lagged year models did not deviate substantially from
same-year annual models.
We demonstrate the relevance of annual DHIs for biodiversity science, as effective predictors of temporal
variation in species richness patterns. We suggest that the use of annual DHIs can improve conservation planning,
by conveying the range of patterns of biodiversity response to global changes, over time.
Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available
Protected areas safeguard biodiversity and provide opportunities for human recreation. However, abundant anthropogenic food subsidies associated with human activities in protected areas can lead to high densities of generalist predators, posing a threat to rare species at broad spatial scales. Reducing anthropogenic subsidies could curb populations of overabundant predators, yet the effectiveness of this strategy is unclear. We characterized changes in the foraging ecology, body condition, and demography of a generalist predator, the Steller’s jay, three years after implementation of a multi-faceted management program to reduce anthropogenic subsidies in a protected area in California. Stable isotope analysis revealed that the proportional contribution of anthropogenic foods to jay diets declined from 88% to 47% in response to management. Overlap between jay home ranges decreased after management began, while home range size, body condition, and individual fecundity remained stable. Adult density in subsidized areas decreased markedly from 4.33 (SE: ±0.91) to 0.65 (±0.20) jays/ha after the initiation of management, whereas density in unsubsidized areas that were not expected to be affected by management remained stable (0.70 ± 0.22 pre-management, 0.58 ± 0.38 post-management). Thus, the response of jays to management was density-dependent such that reduced densities facilitated the maintenance of individual body condition and fecundity. Importantly, though, jay population size and collective reproductive output declined substantially. Our study provides evidence that limiting anthropogenic subsidies can successfully reduce generalist predator populations and be part of a strategy to increase compatibility of species protection and human recreation within protected areas.File: Brunk-et-al-2021_Reducing-anthropogenic-subsidies_Stellers-Jays_Biological-Conservation.pdf
Understanding human influence on ecosystems and their services is crucial to achieve sustainable development and ensure the conservation of biodiversity. In this context, the human footprint index (HFI) represents the anthropogenic impacts on ecosystems and the natural environment. Our objective was to characterize the HFI in Southern Patagonia (Argentina) across the landscape, qualifying the differences among the main ecological areas and especially the forested landscapes. We also assessed the potential utility of HFI to identify priority conservation areas according to their wilderness quality and potential biodiversity values. We created a HFI map (scores varied from 0 representing high wilderness quality to 1 representing maximum human impact) using variables related to direct (e.g. infrastructure) and indirect (e.g. derived from economic activities) human impacts, including settlements, accessibility, oil industry, and sheep production. HFI varied significantly across the natural landscapes, being lower (0.07 0.11) in remote ecosystems close to the Andes Mountains and higher (0.38 0.40) in southern areas close to the provincial capital city. Forested landscapes presented different impact values, which were directly related to the economical values of the different forest types. We determined that the current protected area network is not equally distributed across the different ecological areas and forest types. Priority conservation areas were also identified using the fragmentation produced by the human impact, the patch size, and the potential biodiversity values. HFI can present high compatibility with other land-use management decision making tools, acting as a complement to the existing tools for conservation planning or management.File: Rosas-Y.M.-et-al.-2021.-Human-footprint-defining-conservation-strategies-in-Patagonian-landscapes_J-Nature-Conservation.pdf
Considering their outsized importance as prey for so many species one would assume that patterns of insect abundance and their determinants have been well-studied. On the contrary, insect ecology is poorly understood and documented. Our study sought to gain an understanding of the subgroup of insects that fly, with a particular emphasis on groups that spend part of their life in lakes and streams.
We conducted insect trapping over three years in the forest landscape of northern Wisconsin, near UW-Madison’s Trout Lake Research Station. We trapped insects May-August around five different lakes and identified them in the lab.
There were several patterns that stood out. Flying insects tended to be many times more abundant in nearshore areas compared to interior forests. Different groups of insects showed different patterns. Diptera, including deerflies, midges, and gnats were the most abundant insects overall. As expected, emergent aquatic groups such as midges, mayflies, and dragonflies were more abundant in nearshore areas while beetles and thrips were more abundant in forest interiors. There were also multiple peaks of abundance through the season with large emergence events of midges and mayflies driving much of the pattern. In addition, local canopy cover was negatively correlated with insect abundance.
We observed birds, bats, and fish consuming flying insects. Abundance of these insect predators likely tracks the abundance of their insect prey. In addition, insects perform other ecosystems services such as pollination and nutrient cycling. Understanding the patterns and drivers of insect abundance can help us better understand northern Wisconsin forest ecosystems.
Prioritizing candidate areas to achieve species richness representation is relatively straightforward when distributions are known for many taxa; however, it may be challenging in data-poor regions. One approach is to focus on the distribution of a few charismatic species in areas that overlap with areas with little human influence, and another is to expand protection in the vicinity of existing protected areas. We assessed the effectiveness of these two approaches for protecting the potential distribution of 21 bird species affiliated with the piedmont dry forest in Argentina. We assessed the degree to which current protected areas met the representation target for each bird species. We found that 8% of the piedmont dry forest and 11% of the extent of occurrence of the bird species within piedmont dry forest were protected, indicating a shortfall. Areas with little human influence that overlap with the distribution of charismatic species had a higher number of bird species than areas with high human influence. Areas within the vicinity of protected areas performed similarly to priority areas, but included high human influence areas. We suggest that a prioritization scheme based on areas of charismatic species distribution that overlap with areas of low human influence can function as an effective surrogate for bird species affiliated with the piedmont dry forest in Argentina. Our results have operational implications for conservation planning in those regions of the world where biodiversity data are poor, but where decisions and actions to sustain biodiversity are urgently needed.File: Politi-et-al.-2021-Landscape-and-Urban-Planning.pdf
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
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
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.
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.
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.
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.
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.
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