Spatial Analysis For Conservation and Sustainability
Biodiversity
Species richness varies by orders of magnitude across the globe and so does the richness of endangered species. We are interested in both the macroecological processes causing richness patterns, and the socioeconomic processes determining where biodiversity is threatened.
Medium- and large-sized mammals play important roles in maintaining forest ecosystem functions, and these functions often diminish when mammal species are depleted by human activities. Understanding the sensitivity or tolerance of mammal species to human pressure and detecting species changes through monitoring programs can inform appropriate management decisions. The objective of our study was to identify medium- and large-sized mammal species that can be included in a monitoring programein the Southern Yungas of Argentina. We used occupancy modelling to estimate the probability of habitat use (ψ) of 13 of 25 mammal species detected by 165 camera traps placed in forests across a range of human footprint index (HFI) values. As defined by the HFI, 54% of the study area is wilderness. The probabilities of habitat use of two mammal species were significantly associated with the HFI: the lowland tapir (Tapirus terrestris; ψ = 0.33, range = 0.22–0.50) was inversely associated with HFI values, whereas the grey brocket deer (Mazama gouazoubira; ψ = 0.79, range = 0.67–0.87) was positively associated with the HFI. Monitoring the probability of habitat use of the sensitive species (lowland tapir) could help us to detect changes in areas experiencing anthropogenic impacts before they cause extirpation, whereas the high probability of the habitat use values of the tolerant species (grey brocket deer) might indicate that anthropogenic impacts are strongly influencing habitat, signaling that mitigation strategies might be warranted. The Southern Yungas retains an intact mammal fauna, and we showed that the HFI is useful for monitoring anthropogenic impacts on these mammals. There are still opportunities to develop conservation strategies to minimize threats to mammal species in the region by implementing a monitoring program with the proposed species.
Heterogeneous vegetation supports higher species richness than homogenous vegetation, which is why efficiently identifying heterogenous vegetation can be useful for biodiversity conservation. Satellite remote-sensing data provide an opportunity to generate vegetation heterogeneity metrics and to explore the phenology of vegetation patterns. Phenoclusters are vegetation types with similar phenological characteristics, and valuable for capturing vegetation habitat heterogeneity patterns. Our goal was to map phenoclusters for Wisconsin, USA, at 10-m spatial resolution based on land surface phenology metrics from EVI (Enhanced Vegetation Index) Sentinel-2 data. We characterized each phenocluster based on landcover composition and structure, phenology timing, and environmental factors, and compared them to bird species richness. We also calculated the diversity of phenoclusters at multiple spatial extents. We identified 14 phenoclusters in Wisconsin, each with distinct landcover composition and structure, and unique phenological characteristics. Our remotely-sensed phenoclusters effectively captured environmental gradients, with elevation and temperature emerging as the most important driving variables. Furthermore, the phenoclusters successfully captured bird biodiversity patterns, especially richness of forest and grassland specialist. Our results identified phenological patterns among Wisconsin’s forests, shrublands, and grasslands, capturing phenological timing both among and within the same tree species. Phenoclusters are a valuable tool for capturing vegetation habitat heterogeneity, phenology diversity and biodiversity patterns, as well as climate change effects.
The Global Deal for Nature sets an ambitious goal to protect 30% of Earth’s land and ocean by 2030. The 30×30 initiative is a way to allocate conservation resources and extend protection to conserve vulnerable and under protected ecosystems while reducing carbon emissions to combat climate change. However, most prioritization methods for identifying high-value conservation areas are based on thematic attributes and do not consider vertical habitat structure. Global tall forests represent a rare vertical habitat structure that harbors high species richness in various taxonomic groups and is associated with large amounts of aboveground biomass. Global tall forests should be prioritized when planning global protected areas toward reaching the 30×30 goals. We examined the spatial distribution of global tall forests based on the Global Canopy Height 2020 product. We defined global tall forests as areas with the average canopy height above 3 thresholds (20, 25, and30 m). We quantified the spatial distribution and protection level of global tall forests in high-protection zones, where the 30×30 goals are being met or are within reach, and low-protection zones, where there is a low chance of reaching 30×30 goals. We quantified the protection level by computing the percentage of global tall forest area protected based on the 2017 World Database on Protected Areas. We also determined the global extent and protection level of undisturbed, mature, tall forests based on the 2020 Global Intact Forest Landscapes mask. In most cases, the percentage of protection decreased as forest height reached the top strata. In the low-protection zones,<30% of forests were protected in almost all tall forest strata. In countries such as Brazil, tall forests had a higher per-centage of protection (consistently>30%) compared to forests of lower height, presenting a more effective conservation model than in countries such as the United States, where forest protection was almost uniformly<30% across height strata. Our results show an urgent need to target forest conservation in the greatest height strata, particularly in high-protection areas, where most global tall forests are found. Vegetation vertical structure can inform the decision-making process toward the 30×30 goals because it can be used to identify areas of high conservation value for biodiversity protection which also contribute to carbon sequestration.
Habitat selection is a fundamental behavior of species that shapes a wide range of ecological processes, including species distribution, abundance, nutrient transfer, and tropic dynamics. The study of habitat selection is important to understand the interaction between species and environment. But it is a multivariate and hierarchical process, in which species are distinctively affected by several factors at multiple spatial scales. Therefore, it is important to understand how species select their habitat, what are the important spatial scales, and how the habitat selection process varies for different species.
Figure 1: Conceptual hierarchy of the decision-making process of habitat use by a migratory songbird (Stanley et al 2021).
Hierarchical habitat selection in birds varies greatly by species due to their ecological niches and behaviors. For instance, the Northern Spotted Owl specializes in old-growth conifers for nesting, forages in mature forests, and prefers undisturbed landscapes for its home range. Conversely, the Kirtland’s Warbler prefers, early to mid-successional jack pine forests, growing on sandy soil for nesting, these forests provide the specific vegetation structure and insect abundance that are essential for their foraging needs. Studying habitat selection is therefore crucial for effective conservation and ecosystem management, as it provides insights into their ecological requirements and aids in preserving their populations and the overall health of ecosystems.
Figure 2: Kirtland’s Warbler (left) Spotted Owl (right)
Despite notable advancements in the field, our understanding of the hierarchical aspects of habitat selection in birds remains limited. Habitat selection models typically rely on satellite data from a single sensor and scale, which limits their effectiveness in capturing spatial patterns of bird habitat.
Akash Anand is currently conducting a study aimed at modeling multiscale hierarchical habitat selection in birds and explaining the factors influencing individual species’ choices. His research investigates the crucial spatial scales for different species and identifies local environmental features that play a pivotal role in overall habitat selection decisions. To achieve this, he employs deep learning models to gain insights into the intricate interactions between species and their environments.
In conclusion, Akash’s research aims to determine the crucial spatial scales for individual species, providing valuable insights for conservationists and policymakers. Additionally, the findings will provide evidence of how the same species respond to varying environmental conditions and how their choices differ in different scenarios. This knowledge will inform more effective conservation and management strategies.
Closing the research-implementation gap is key for advancing biodiversity conservation. One approach is to generate ecologically relevant spatial datasets that integrate easily with existing management plans. Our goal was to identify priority forest conservation areas in Argentina by combining species distributions, human footprint data, and existing forest zoning. We: (i) mapped potential habitat distributions of 70 plant and animal species associated with forests, and of recognized social and ecological importance, (ii) combined the species distributions with human footprint data to identify priority conservation areas, and (iii) evaluated the juxtaposition of our priority conservation areas with current forest management zones. We found that priority conservation areas (i.e., high number of species and low human footprint) are poorly protected by the current zoning scheme. While the Andean-Patagonian region had a substantial portion (57 %) of priority conservation areas in high protection zones, in four other forest regions we evaluated, only 16–37 % of priority areas had high protection levels. Of great concern are the Chaco and Espinal regions, where 36 % and 39 %, respectively, of priority conservation areas are in low protection zones, where conversion to other uses (row crops, livestock) is allowed. Our results provide new spatial information to managers and conservationists highlighting where current forest zoning performs well, and where it may warrant re-evaluation. Overall, our study highlights the value of integrating species distributions and human footprint maps into existing land use plans to guide conservation efforts in data-poor countries, and is an example of a strategy for closing the research-implementation gap.
Identifying the factors that determine the spatial distribution and habitat use of species of conservation importance is essential to developing effective conservation and management strategies. As seed dispersers, guans play a key role in the regeneration of forests in South America and are threatened mainly by habitat loss and hunting pressure. The Yungas Guan Penelope bridgesi, an endemic species restricted to the Southern Yungas of Argentina and Bolivia, has been recently recognized as a separate species. To determine the conservation status of Yungas Guan, information on its distribution and habitat use is urgently needed. The objectives of our work were to 1) determine the potential distribution of the Yungas Guan in the Southern Yungas of Argentina and 2) assess the influence of environmental and anthropogenic covariables on habitat use of the species. We used records of Yungas Guan to model the potential distribution of the species with MaxEnt software and developed occupancy models to determine habitat use and influential elements of the landscape (puestos, urban areas, roads, rivers, and elevation). We obtained data on the presence of Yungas Guan with camera traps, with an effort of 6,990 camera trap-days. The total potential distribution of the species was 21,256 km2.We found that the habitat use by Yungas Guan increased with proximity to rivers and streams. The probability of habitat use was 0.27, with a range of 0.02–0.42.Of the total potential distribution area, 15,781 km2 (81%) had a probability of habitat use greater than 0.2. This study is the first in determining the potential distribution of Yungas Guan in the Southern Yungas of Salta and Jujuy provinces in Argentina and highlights the importance of conducting analyses with occupancy models to assess the influence of environmental and anthropogenic variables and threats to cracid species.
By schlorian 13 May 2019 https://www.schlorian.ch/cartoon-der-woche-2019/
Species and populations are declining rapidly, with over 3 billion birds lost in the past 50 years. Astoundingly, the US is on track to lose 50% of its remaining individual birds in 50 more years without intervention (stateofthebirds.org/2022/). Birds, unfortunately, are not alone, as 40% of all species are projected to face extinction by the end of this century. Despite these alarming numbers, conservation spending in the US has remained relatively stable over the past years – roughly $6-7 billion with few exceptions. Therefore, one of the most challenging questions for scientists is where will conservation action – and protected areas in particular – do the most to protect species of conservation concern.
Kathleen Carroll’s current work in the SILVIS Lab compares various biodiversity metrics, each with unique assumptions, to my previous maps of threatened/endangered and decreasing species (see my previous webstory for more on that project). I can use these comparisons to evaluate how well these additional metrics, which are usually treated as direct surrogates for biodiversity, capture the conservation patterns necessary to protect threatened or endangered species. I also will evaluate which, if any, combinations of these metrics work best to inform conservation planning on regional and national scales. To do this, I will model all metrics for the US and then compare them directly to my threatened/endangered species data. I will do so using Marxan, a conservation planning problem support tool, to create nationwide maps that identify conservation priority areas. These maps, one for each metric, will include a certainty estimate based on pixel importance across data layers and identify gaps in protected areas. By comparing different metrics, we will be provided maps of high-certainty high-priority areas where land managers and agencies can focus on endangered species conservatio through designation of new protected areas. From https://www.maxansolutions.org
Landscapes are undergoing continuous transformation, with both natural and human factors causing the destruction of some habitats and the formation of others. While wildlife can adapt to natural changes, the current scale of human-made landscape alterations is much greater than nature’s ability to adapt. Some species can thrive in human-made landscapes, but others are at risk of extinction due to habitat loss.
Azerbaijan, a country in the Caucasus region with rich biodiversity and a long history of human-driven land cover changes. For conservation and sustainable management there, it is critical to understand the impacts of landscape changes on wildlife habitats. The changes in the Caucasus eco-region have accelerated in the 20th century due to population growth and Soviet nature-transformation efforts. A new study by Afag Rizayeva aims to understand the impact of these changes on the habitats of eight ungulate species, including common animals like wild boar and roe deer, as well as a rare species of gazelle. With landscapes constantly changing due to natural and human causes, it is increasingly important to understand how these changes are affecting wildlife populations. East Caucasian tur – Capra cylindricornis by Azerchin Muradov
Afag has developed the Caucasus land cover maps for the 1960s using former spy satellite images (Corona) and has analyzed the long-term changes in these landscapes using recent land cover maps derived from Landsat images. Her research begins by using the presence data of eight ungulate species, conducting species distribution modeling to evaluate their current ranges and the landscape features that are most important to each species. Red deer – Cervus elaphus by Azerchin Muradov
Next, Afag will analyze the changes in land cover within each species’ range, determining stable areas, habitat gains and losses, and assessing the positive or negative effects of these changes on the species’ habitats. This will enable her to determine if the species can continue to use the same areas despite human activities, or if they require urgent land management solutions to protect them. The results of Afag’s study will help guide wildlife conservation planning and will be used by local NGOs and government agencies. As human impact on nature is a global issue, the methodological approach she develops in her research will have applications in other regions facing similar issues. Chamois – Rupicapra rupicapra by Azerchin Muradov
In conclusion, understanding the impact of long-term land cover changes on wildlife habitats is crucial for conservation and sustainable management. The research being conducted by Afag Rizayeva will provide valuable insights into this issue and help guide efforts to protect the wild species in Azerbaijan and beyond. Stay tuned!
Classifying forests at tree species level from remotely sensed data over large areas is challenging, especially when ground-data do not exist. Since the opening of the Landsat archive in 2008, opportunities to improve forest type mapping and classification have increased, making it possible to explore phenological properties across different forest types. The seasonal dynamics of vegetation indices (e.g., enhanced vegetation index, EVI) are well correlated with the seasonal dynamics in photosynthetically active leaf area and are a good proxy for phenological stages. In addition to being helpful for individual forest type and tree species classification, phenology is linked to landscape resources because vegetation phenology determines food availability for a wide range of forest species.
To improve broad-scale forest mapping and landscape characterization, we developed an approach that can categorize forests based on both land surface phenology and climate characteristics, the forest phenoclusters. We calculated land surface phenology metrics based on EVI Sentinel-2 and EVI Landsat 8 combined annual time series. We also derived land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8 and used precipitation from the WorldClim dataset. We then performed stratified X-means classification followed by hierarchical clustering. We applied the methodology in Argentina (2.8 million km2), which has a wide variety of forests, from rainforests to cold-temperate forests. We characterized the forest phenoclusters based on land surface phenology and climate characteristics, as well as based on strong regional expert knowledge.
We identified 54 forest phenoclusters across Argentina (Figure 1), each with unique combinations of vegetation phenology and climate characteristics. The resulting map is a valuable source of novel and ecologically relevant information applicable to management and conservation of biodiversity, for example, for stratifying biodiversity assessments, supporting wildlife habitat mapping and to improve landscape planning, including development of new reserves strategies. Additionally, using our method, it is possible to estimate phenoclusters at a variety of scales, which makes them useful for a variety of modeling applications. Specifically, forest phenoclusters could be an input data set to benefit species distribution modeling greatly over large areas with low data availability, such as for tapirs (Tapirus terrestris) and jaguars (Panthera onca) that occupy several ecoregions in Argentina. Figure 1. The 54 classes of forest phenoclusters across Argentina and some examples in five regions. ESP, Espinal; HDC, Humid and Dry Chaco; HM, High Monte; MAF, Misiones Atlantic; PAT, Continental Patagonian forests; PFS, Parana flooded savanna; SAY, Southern Andean Yungas; TDF, Patagonian forests of Tierra del Fuego.
Climate variability affects the phenology of vegetation and the seasonality of temperature, which can lead to mismatches between species and resources. When species are not able to track phenology and seasonal temperate changes, populations decline, posing a threat to biodiversity. Many plants and animals synchronize the timing of their life events with vegetation phenology. Mismatches in the timing of such events, can entail, for example, food limitations when the peak of bird nestling growth is not timed to occur during the annual peak in caterpillar abundance, which may affect reproductive success. In contrast, high spatial variability enhances ecological resilience to biodiversity loss from high inter-annual variability. Biodiversity should benefit from high spatial variability in vegetation greenness and land surface temperature within intact habitats, because such spatial variability is indicative of a variety of resources in close proximity and increases the likelihood that suitable conditions are available during times of extremes. Eduarda Silveira, a postdoctoral research in the SILVIS lab, recently published a study with her colleagues describing their efforts to identify hotspots of biodiversity conservation concern due to threats from high inter-annual variability (Figure 1). Figure 1. Potential integrations of inter-annual and spatial variability in vegetation greenness and land surface temperature, and the level of conservation concern for each integration.
They generated inter-annual and spatial remotely sensed indices based on time series analysis and image texture, respectively, and integrated these indices to identify areas of high, medium and low conservation concern (Figure 2). Figure 2. Areas of high and low conservation concern based on (1) vegetation greenness and (2) land surface temperature: (a) inter-annual variability in phenology, (b) spatial variability, (c) integration between inter-annual and spatial variability, and (d) hotspots maps.
They applied their method in Argentina. They identified hotspots of conservation concern in parts of northeastern and southern Argentina. These are sites where management efforts could be valuable (Figure 3). Eliminating existing pressures (i.e., dam construction, land use change) and improving spatial variability by increasing the abundance and diversity of natural landcover in these highly modified regions are promising approaches to increase resilience to climate extremes for native wildlife species. In contrast, areas in the northwest and central-west have high spatial variability, which may confer resilience to climate extremes, due to the variety of conditions and resources within close proximity (Figure 3). Adding protected areas in these naturally resilient regions may be effective in both protecting current patterns of biodiversity and maintaining their adaptive capacity to climate change. Eduarda Silveira hopes that her results will help Argentina’s conservation leaders to be strategic in their protection decision and to prioritize conservation management actions. Figure 3. Hotspots of highest and lowest conservation concern in Argentina.
