Prioritizing global tall forest toward the 30-by-30 goals

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.

File: Conservation-Biology-2023-Huang-Prioritizing-global-tall-forests-toward-the-30-30-goals.pdf

Performance of novel remotely-sensed variables in maps of bird species distributions in Argentina

Mitred Parakeet (Psittacara mitratus) eating the fruit of a Schinus sp. tree
Nothofagus forest in Tierra del Fuego, Argentina.
Nothofagus forest in Tierra del Fuego, Argentina.

Halting biodiversity declines and promoting sustainable ecosystem usage are major conservation goals. To do so, it is necessary to understand the environmental correlates of biodiversity patterns.

Environmental variables used in biodiversity modelling come from a variety of sources and have varying levels of power to explain distributions of different species. Many environmental variables that have been used regularly for many years have shortcomings- they may not cover large areas, may not capture suitable habitat, or may not be able to capture changes in environmental conditions over time or space. Increasingly, novel remotely-sensed environmental data are being developed for modelling biodiversity patterns. Novel remotely sensed products may complement or even offer better results than environmental variables that have been used for many years.

Olah set out to identify sets of complementary variables, from among a set of standard variables and newly created variables, that can improve species distribution modelling. Olah used a combination of land cover, elevation, precipitation, and temperature variables, that are commonly used in species distribution modelling, and a set of novel-remotely sensed products to model distributions of forest affiliated bird species in Argentina.

Yungas forest in Calilegua National Park, Jujuy, Argentina
Yungas forest in Calilegua National Park, Jujuy, Argentina

The set of novel environmental variables were created by SILVIS lab postdoctoral researcher Eduarda Silveira. These products measure spatial and interannual variation in the phenology of land surface temperature and forest vegetation greenness. Olah predicted that areas with more spatial variability in phenology and thermal conditions are more likely to host more species because there are a variety of resources and thermal conditions in close proximity, allowing many species to coexist in a small area. These areas may also buffer against high year-to-year variation in conditions because organisms are more likely to have access to refugia or resources that could allow them to persist. Temporal variation in forest greenness or temperature describes how consistent conditions are between years. High variability means that phenological events are not occurring at a predictable time, while low variability means that events are occurring predictably each year.

In another new product developed by Silveira, ground forest inventory data was combined with radar-based remotely-sensed data, resulting in modelled forest structure wall-to-wall across Argentina. Silveira also developed maps of forest phenoclusters and phenocluster diversity. Phenoclusters classify different forest types in Argentina, based on vegetation phenology, land surface temperature, and precipitation. Olah thinks phenoclusters are a more ecologically relevant way to characterize habitat important to bird species than typical land cover maps. Phenoclusters capture functional rather than only compositional or structural characteristics. By comparing how well these novel remotely-sensed products and traditionally used variables perform in species distribution modelling Olah assessed their usefulness for biodiversity mapping.

Polylepis forest in an Andean Mountain valley, Jujuy, Argentina.
Polylepis forest in an Andean Mountain valley, Jujuy, Argentina.

Olah developed species distribution models for 152 forest bird species. She found that among three sets of models she constructed, those containing novel, traditional, or a mixed set of variables, performance was similar. However, models constructed from the mixed set of variables performed slightly better than models containing only one or the other set of data. The variables that were included in the greatest number of individual species’ distribution models included precipitation seasonality, precipitation of the driest quarter, as well as spatial heterogeneity in winter land surface temperature, which is a novel variable. Her results highlight how variables derived from different sources can offer complementary information for biodiversity modelling. Her models contribute to forest harvest planning in Argentina.

Closing the research-implementation gap: Integrating species and human footprint data into Argentina’s forest planning

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.

File: Martinuzzi-et-al-2023_Closing-research-implementation-gap.pdf

Distribution and habitat use of the endemic Yungas Guan Penelope bridgesi in the Southern Yungas of Argentina

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.

File: Tejerina-et-al-2022-BCI.pdf

Arctic waterfowl migration through Eurasian steppe: how to catch short-term environmental conditions and identify key migratory habitats using satellite images

Long-distance migrations are an important part of the life cycle for most Arctic waterfowl. The birds spend several months each year between their breeding and wintering places, with most of this time staying at their stopover sites to rest and feed. Habitat quality at the stopovers determine subsequent survival during migrations and reproductive success. Two questions present themselves: where are the key waterfowl habitats along their migratory routes, and what landscape features make these places vitally important? Understanding this is crucial for bird protection and population management.

Fig. 1 Bewick’s Swan
Fig. 1 Bewick’s Swan

Satellite images are used widely for landscape research; however, their application to bird migration studies is challenging. First, environmental conditions within a large area are not the same during migration period, so there is no single time window suitable to select satellite images for the entire area. Second, favorable conditions at each stopover site are short-term, as birds spend only a few weeks at each site. The needed satellite images may be unavailable for those exact periods due to weather conditions (clouds etc.), making it difficult to obtain sufficient data for a single migratory season. Third, environmental conditions and migration time could vary by several weeks depending on weather of a given year, which, in turn, impedes combining images from different years.

Natalia studied how to use satellite images for delineating key migratory habitats using the example of spring migration of Bewick’s Swan in Eurasian steppe (fig. 1). To overcome the limitation related to satellite image availability, Natalia used snow melt as a synchronizing point. In practice, specific dates on which the swans appear at one or another area do not really matter because the birds will move farther north as soon as ice-free water and food become available there. In other words, they arrive soon after snow melt and stay a couple of weeks until more northern areas become snow-free.

Natalia used daily MODIS data to identify where snow melted each year in different parts of the swan’s migratory flyways and then filtered Landsat images using that information. This allowed her to combine Landsat images for all migration areas from different years at the same phenological phase. This approach made it possible to produce accurate and detailed landscape maps demonstrating what environmental conditions prevailed at stopovers at exactly the time when the swans were present. With these maps species distribution modeling can delineate key swan’s habitats.

Fig.2 Key habitats during summer
Fig.2 Key habitats during summer

The maps have revealed the critical landscape feature important to the birds: numerous local depressions scattered across croplands (fig. 2). In summer these appear as a part of the agricultural landscape (only the smallest, lowermost places may not be ploughed and get overgrown with wildflowers) and are hardly detectable (fig. 3, e) on satellite images. In spring, however, they accumulate melted water to become shallow temporary water bodies (fig. 3, a-c). These flooded depressions provide migratory birds with food and refuge so that the waterfowl do not need to move between roosting and feeding sites. They are also available 10-15 days before ice-out on lakes, allowing birds to migrate and potentially reach breeding grounds earlier.

Fig.3 Changes in open water in the steppe during spring. RGB: SWIR 1, NIR, Red bands. Red circle –flooded fields, yellow square -permanent lake. Bright blue color indicates snow/ice, black color indicates open water.
Fig.3 Changes in open water in the steppe during spring. RGB: SWIR 1, NIR, Red bands.
Red circle –flooded fields, yellow square -permanent lake.
Bright blue color indicates snow/ice, black color indicates open water.

More generally, Natalia’s research provides a useful approach to understanding the key short-term conditions that birds rely on during migrations. Natalia’s results have important implications for conservation efforts, such as the creation of protected areas and free hunting zones and adjusting land management in agricultural lands.

Comparing Approaches to Identify Protected Areas for Wildlife across the U.S.

By schlorian 13 May 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 ( 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.


How has half a century of land cover changes altered habitats of ungulates in the biologically diverse Caucasus?

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
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!

Forest phenoclusters for Argentina based on vegetation phenology and climate

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.

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

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.

The Benefits of Satellite Data for Wildlife Species Distribution Models Across the US

For centuries, humans have recognized that our collective actions modify and shape the world around us. These actions also have direct and lasting impacts on the plants and animals that many communities rely on for their livelihoods ─ often referred to as ecosystem services. For example, in the Rocky Mountain West, local communities rely on provisioning from local wildlife (e.g., hunting), safe water and air from local plants and snowmelt, ecosystem cultural services for tribal communities, natural soil creation for agriculture, pastures for grazing livestock, and income from tourists seeking to fish, hike, hunt, ski, and view local wildlife. Similarly, the Great Lakes wetlands provide fisheries habitat that supports wildlife and people, space for recreational activities and tourism, hydro and wind power, shoreline protection, sediment trapping, and storage for nutrients and carbon. The ecosystem services provided by any one region, such as the Rocky Mountain West or the Great Lakes, are intrinsically tied to the health of the species and people in that community. Without plants and animals, many of these services could suffer. Therefore, one of the most challenging questions for scientists is how to ensure that our science advances conservation and subsequently bolsters ecosystem services that support local communities.

A small sample of the diversity of birds in the lower 48. There are an estimated > 1,100 species of birds in the United States. Photo source: Creative Commons.

In the SILVIS Lab, Kathleen Carroll identifies and averts biodiversity loss by developing complex models to predict and examine biodiversity patterns across the US. Biodiversity, which measures the variety of life in an area, is essential to conservation. If biodiversity declines, there is a risk to both species and ecosystem services. By modeling and predicting biodiversity over broad areas, we can determine where the biggest threat to species is. While this approach is simple in theory, extensive information about where species are and what they select habitat based on is key. Researchers have been limited by access to data. The SILVIS lab has developed new satellite indices, which provide detailed datasets that I am using in complex models. My preliminary results show higher predictive power from relatively new machine learning models, called randomForest models, compared to other approaches. Access to the new satellite datasets and complex models helps me to 1) further evaluate the value of fine-scale satellite data for biodiversity mapping, 2) develop predictive biodiversity models, and 3) provide maps to land trusts and communities to help them protect species and ecosystem services.

Preliminary map of bird species richness – a biodiversity metric. Photo credit: Dave Helmers.