Spatial Analysis For Conservation and Sustainability
Conservation
Biodiversity is threatened, and conservation is urgent. The reality of limited resources for conservation requires prioritization among actions, species, and places, and the building of capacity in countries where threats are high.
As humanity is facing the double challenge of species extinctions and climate change, designating parts of forests as protected areas is a key conservation strategy.1–4 Protected areas, encompassing 14.9% of the Earth’s land surface and 19% of global forests, can prevent forest loss but do not do so perfectly everywhere. 5–12 The reasons why protection only works in some areas are difficult to generalize: older and newer parks, protected areas with higher and lower suitability for agriculture, and more and less strict protection can be more effective at preventing forest loss than their counterparts.6,8,9,12–16 Yet predicting future forest loss within protected areas is crucial to proactive conservation. Here, we identify an early warning sign of subsequent forest loss, based on forest loss patterns in strict protected areas and their surrounding landscape worldwide, from 2000 to 2018.17,18 We found that a low level in the absolute forest cover immediately outside of a protected area signals a high risk of future forest loss inside the protected area itself. When the amount of forest left outside drops to <20%, the protected area is likely to experience rates of forest loss matching those in the wider landscape, regardless of its protection status (e.g., 5% loss outside will be matched by 5% loss inside). This knowledge could be used to direct funding to protected areas threatened by imminent forest loss, helping to proactively bolster protection to prevent forest loss, especially in countries where detailed information is lacking.
Conserving the remaining wildest forests is a top priority for conservation, and human footprint maps are a practical way to identify wild areas. However, available global assessments of wild areas are too coarse for land use decisions, especially in countries with high deforestation rates, such as Argentina. Our main goal was to map the human footprint in Argentina’s forested areas to improve conservation planning at regional and country levels. Specifically, we quantified the level of human influence on the environment and mapped the wildest native forests (i) across forest regions, and (ii) in the different land-use categories of the National Forest Plan, which is a key policy instrument for conserving the nation’s native forests through zoning, and (iii) identified wildest forests that are at risk due to human activities. We analyzed detailed spatial data on settlements, transportation, energy, and land use change, and estimated the areal extent to which these various human activities disrupt natural processes. We defined pixels with human footprint index of zero as wildest areas. We found that a substantial portion (43%) of Argentina’s forested area remains wild, which suggests there are opportunities for conservation. However, levels of human influence varied substantially among forest regions, and Atlantic and Chaco forests have the highest levels of human influence. Further, we found that the National Forest Plan does not conserve the wildest forests of the nation, as most (78%) of the wildest native forests are located in zones that allow silvopasture, timber production, and/or forest conversion to crops, thus potentially threatening biodiversity in these areas. Our map of wildest forests is an important, but first, step in identifying wildland forests in Argentina, as available spatial data layers of human activities capture many, but not all, human influences on forests. For instance, small human features, like certain rural roads, trails, and rural settlements exist in our wildest areas. Our study provides new datasets to assist land use planners and conservationists, and identifies areas for conservation attention in Argentina. More broadly, our analyses highlight the value of detailed human footprint data to support conservation decisions in forest landscapes.
Habitat Conservation Plans (HCPs) commonly facilitate habitat conservation on private land in the United States, yet the effectiveness of individual HCPs is rarely evaluated. Here, we assess the effectiveness of a high-profile HCP created by a lumber company to protect old-growth forests used for breeding by Marbled Murrelets (Brachyramphus marmoratus) on private land. We used 17 years of HCP-monitoring data to compare trends in murrelet occupancy and inland counts between private HCP areas and public reference areas over time. Based on occupancy models applied to audio-visual survey data, average occupancy was higher in public reference areas (0.85; 85% confidence intervals [CI]: 0.79–0.90) than in private HCP areas (0.46; 85% CI: 0.38–0.54). Numerically, trends in occupancy were slightly positive in public areas ( = 1.01; 85% CI: 0.94–1.08) and slightly negative in private areas ( = 0.97; 85% CI: 0.87–1.06), but CI did not preclude stable occupancy on both ownerships. Based on generalized linear mixed models applied to inland radar survey data, murrelet counts in private HCP areas (least-squares [LS] mean = 8.7; 85% CI: 6.2–12.2) were lower than those in public reference areas (LS mean = 14.8; 85% CI: 10.1–21.7), but CI overlapped. Murrelet counts declined by 12–17% annually on both ownerships over the study period based on the top model, but a closely competing interactive model suggested more rapid declines in public reference (14–20%) than in private HCP (10–15%) areas. Both models indicated that murrelet counts were negatively related to sea surface temperature, suggesting that warm ocean conditions negatively affect murrelet breeding effort. Collectively, these results suggest that while HCP habitat may be lower quality than public reference areas, the HCP has likely not exacerbated ongoing declines of murrelets in the region. This work highlights the importance of including reference areas when evaluating conservation policies.
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.
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.
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.
Insect trapping took place in the northern highland lake district. The study area consists of northern mixed forest with one of the highest densities of lakes in the world.
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.
Mean insect abundance varied over the course of the summer with peak average insect abundance occurring in late May and early June (Julian Day 145-155) in both years.
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.
Grassland birds have exhibited dramatic and widespread declines since the mid-20th century. Greater prairie chickens (Tympanuchus cupido pinnatus) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007–2009 and 2014–2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of greater prairie chickens in central Wisconsin, USA. We examined habitat, nest-site, and brood-rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life-history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood-rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15%–20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life-history stages and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high-quality habitat for greater prairie chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie chickens in central Wisconsin, but trade-offs among life-history stages and the timing of management practices must be considered carefully.
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 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.
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.
