Evaluating bird population trends requires baseline data. In North America the earliest population data available are those from the late 1960s. Forest conditions in the northern Great Lake states (U.S.A.), however, have undergone succession since the region was originally cut over around the turn of the twentieth century, and it is expected that bird populations have undergone concomitant change. We propose pre-Euro- American settlement as an alternative baseline for assessing changes in bird populations. We evaluated the amount, quality, and distribution of breeding bird habitat during the mid-1800s and early 1990s for three forest birds: the Pine Warbler ( Dendroica pinus), Blackburnian Warbler ( D. fusca), and Black-throated Green Warbler ( D. virens). We constructed models of bird and habitat relationships based on literature review and regional data sets of bird abundance and applied these models to widely available vegetation data. Original public-land survey records represented historical habitat conditions, and a combination of forest inventory and national land-cover data represented current conditions. We assessed model robustness by comparing current habitat distribution to actual breeding bird locations from the Wisconsin Breeding Bird Atlas. The model showed little change in the overall amount of Pine Warbler habitat, whereas both the Blackburnian Warber and the Black-throated Green Warbler have experienced substantial habitat losses. For the species we examined, habitat quality has degraded since presettlement and the spatial distribution of habitat shifted among ecoregions, with range expansion accompanying forest incursion into previously open habitats or the replacement of native forests with pine plantations. Sources of habitat loss and degradation include loss of conifers and loss of large trees. Using widely available data sources in a habitat suitability model framework, our method provides a long-term analysis of change in bird habitat and a presettlement baseline for assessing current conservation priority.
File: Schulte-et-al.-2005-ConsBiology.pdf
Frequent, small wildfires can pose dangers to homes in the wildland–urban interface, but are not often included in wildfire hazard models. We assessed patterns of small wildfire occurrence probability in the Northeast region of the United States, focusing on (1) spatial and seasonal variations; (2) differences between small and large fires (size threshold of 4 ha); and (3) how predicted probabilities are influenced by inconsistent wildfire definitions in urbanised landscapes. We analysed fire incident report data from 2005 to 2017 to parameterise maximum entropy (MaxEnt) models based on land cover, topography, climatic water deficit, soil moisture and road density. Overall, wildfire occurrence was highest in areas with lower agricultural cover and with more low-density urban development (explaining 53.5 and 28.6% of variance, respectively, in our region-wide model), while larger fires were concentrated in areas with intermediate levels of development, higher climatic water deficit and more rugged topography. These patterns were largely consistent when we assessed models for individual states, but differences in wildfire reporting patterns led to differences in the effect of urban development on fire probability. Our results provide novel understanding of small wildfire patterns in the Northeast and demonstrate the need to more reliably quantify these hazards.
File: nrs_2021_carlson_001.pdf
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.
File: mmc3.pdf
The ecosystem services that forests provide depend on tree species composition. Therefore, it is important to map not only forest extent and its dynamics, but also composition. Open access to Landsat has resulted in considerable improvements in remote sensing methods for mapping tree species, but most approaches fail to perform when there is a shortage of clear observations. Our main goal was to map forest composition with Landsat imagery in various data availability conditions, and to investigate how the missing data, either due to clouds or scan line problems affect classification accuracy. We tested a data driven approach that is based on multi-temporal analysis of the tree species’ spectral characteristics making it applicable to regional-scale mapping even when the gap-free imagery is not available. Our study area consisted of one Landsat footprint (26/28) located in Northern Wisconsin, USA. We selected this area because of numerous tree species (23), heterogenic composition of forests where the majority of stands are mixed, and availability of high-quality reference data. We quantified how classification accuracy at the species level was affected by a) the amount of missing data due to cloud cover and Scanning Line Corrector (SLC) gaps, b) the number of acquisitions, and c) the seasonal availability of images. We applied a decision tree classifier, capable of handling missing data to both single- and a three-year Landsat-7 and Landsat-8 observations. We classified the dominant tree species in each pixel and grouped results to forest stands to match our reference data. Our results show four major findings. First, producer’s and user’s accuracies range from 46.2% to 96.2% and from 59.9% to 93.7%, respectively for the most abundant forest types in the study area (all types covering greater than 2% of the forest area). Second, all tree species were mapped with overall accuracy above 70% even in when we restricted our data set to images having gaps larger than 30% of the study area. Third, the classification accuracy improved with more acquisitions, especially when images were available for the fall, spring, and summer. Finally, producer’s accuracies for pure-stands were higher than those for mixed stands by 10 to 30 percentage points. We conclude that inclusion of Landsat imagery with missing data allows to map forest types with accuracies that previously could be achieved only for those rare years for which several gap-free images were available. The approach presented here is directly applicable to Landsat-like observations and derived products such as seasonal composites and temporal statistics that miss 30% or more of the data for any single date to develop forest composition maps that are important for both forest management and ecology.
File: 1-s2.0-S0303243422000150-main.pdf
Armed conflicts and major political changes can result in the forced displacement of thousands of people and may have substantial effects on the environment. However, it is difficult to predict and mitigate long-term consequences of such displacements, especially when they trigger abrupt land-use changes that result in a regime shift of the land-use system. Our main goal was to determine the effects of post-WWII forced displacements on long-term landscape dynamics in the Polish Carpathians. After World War II, 630,000 Ukrainians were forcibly displaced from southeastern Poland, leading to permanent depopulation of mountain borderlands. We conducted a village-level analysis of forest area change across the Polish Carpathians (1685 villages/cadastral communities), and a detailed analyses of landscape change and land-cover trajectories in two highly depopulated test sites. Our source data were pre-war (1850s–1860s and 1930s) and post-war (1970s and 2010s) census data and topographic maps. We found a substantial forest area increase after displacements, far outpacing the widely reported forest increase due to the collapse of socialism in early 1990s, and a striking landscape simplification. Astonishingly, almost two thirds of the post-war (1930s–1970s) forest area increase in the entire Polish Carpathians (115,000 ha out of 181,000 ha) was due to the forced displacements. The land-use regimes shifted from being agriculturally-dominated to being forest-dominated, and approached a stable alternative state. As a result, a once densely populated rural region has become one of the largest ‘wilderness’ areas in Central Europe, with vast areas void of human settlements and resurgent wildlife populations. This highlights that forced displacements, which are common during and after armed conflicts, can have substantial and long-lasting effects on land use.
File: 1-s2.0-S0169204621001274-main.pdf
The wildland-urban interface (WUI), where housing is in close proximity to or intermingled with wildland vegetation, is widespread throughout the United States, but it is unclear how this type of housing development affects public lands. We used a national dataset to examine WUI distribution and growth (1990–2010) in proximity to National Forests and created a typology to characterize each National Forest’s combination of WUI area and housing growth. We found that National Forests are hotspots for WUI growth, with a 38% increase in WUI area and 46% growth in WUI houses from 1990 to 2010, in excess of WUI growth for the conterminous U.S. Growth within National Forests was higher than the surrounding area. Diffuse intermix WUI, where houses are intermingled with wildland vegetation, is common within National Forests, but WUI houses around National Forests were primarily in denser interface WUI areas, which lack substantial wildland vegetation. WUI was more prevalent within and around National Forests in the East, while National Forests in the West experienced higher rates of WUI growth. National Forests with the most challenging WUI issues—extensive WUI area and rapid growth in intermix and interface—were found primarily in the South and interior West. Given the diversity of WUI landscapes, effectively responding to current and future WUI challenges will require both engagement with individual homeowners dispersed throughout National Forests, as well as increased emphasis on mitigating denser interface development around National Forests. At a time when wildfire risks are expected to intensify due to climate change, and 75% of privately owned land within and around National Forests is not yet WUI, understanding WUI growth patterns in proximity to public lands is vital for land management and human wellbeing.
File: 1-s2.0-S0169204621002462-main.pdf
Secondary cavity nesters, bird species that rely on the presence of existing cavities, are highly vulnerable to anthropogenic and stochastic processes that reduce the availability of cavity bearing trees. The most common logging practice in Neotropical forests is selective logging, where a few valuable tree species are logged, primarily old, large trees that are the most prone to develop cavities and produce larger amounts of fruits and seeds. Tucuman Amazon, Amazona tucumana, is a threatened parrot that relies on the tree-cavities and food provided by large, old trees. Our objective was to evaluate how logging affects 1) stand and nest plot forest structure, 2) nesting site selection, 3) food availability, 4) density of suitable cavities, 5) nest density, and 6) nest spatial pattern of Tucuman Amazon by comparing a mature undisturbed forest in a National Park (NP) vs a logged forest (LF). We determined the availability of suitable cavities and food resources consumed by Tucuman Amazon, and we compared nest density and spatial pattern of nests between NP vs LF. The Index of food availability for all tree species consumed by Tucuman Amazon and for P. parlatorei were significantly higher in NP than in LF (34.5 ±13.3 m ha− 1 vs. 3.5 ± 1.0 m ha− 1 and 5.6 ± 2.3 m ha− 1 vs. 1.2 ± 1.0 m ha− 1, respectively). Density of suitable cavities for nesting in the NP was significantly higher than in the LF: 4.6 cavities ha− 1 [C.I. 95 %: 3.07 – 7.04 cavities ha− 1] vs. 1.1 cavities ha− 1 [C.I. 95 %: 0.73 – 1.66 cavities ha− 1], respectively. Mean density of Tucuman Amazon nests was significantly higher in the NP than in LF (0.25 ± 0.04 vs. 0.06 ± 0.04 nest ha− 1, respectively). Food availability is an important factor that affects Tucuman Amazon populations and when food is not limiting, the availability of suitable cavities and territorial behavior could play a role in regulating nest density. When evaluating the limiting factors for secondary cavity-nesting species of conservation concern it is important to evaluate the interplay of a set of potential limiting factors to propose sound forest management recommendations.
File: Rivera-et-al-2022_Effect-of-logging-on-Tucumon-Amazon.pdf
Grasslands are important for global biodiversity, food security, and climate change analyses, which makes mapping and monitoring of vegetation changes in grasslands necessary to better understand, sustainably manage, and protect these ecosystems. However, grassland vegetation monitoring at spatial and temporal resolution relevant to land management (e.g., ca. 30-m, and at least annually over long time periods) is challenging due to complex spatio-temporal pattern of changes and often limited data availability. Here we assess both shortand long-term changes in grassland vegetation cover from 1987 to 2019 across the Caucasus ecoregion at 30-m resolution based on Cumulative Endmember Fractions (i.e., annual sums of monthly ground cover fractions) derived from the full Landsat record, and temporal segmentation with LandTrendr. Our approach combines the benefits of physically-based analyses, missing data prediction, annual aggregations, and adaptive identification of changes in the time-series. We analyzed changes in vegetation fraction cover to infer the location, timing, and magnitude of vegetation change episodes of any length, quantified shifts among all ground cover fractions (i.e., green vegetation, non-photosynthetic vegetation, soil, and shade), and identified change pathways (i.e., green vegetation loss, desiccation, dry vegetation loss, revegetation green fraction, greening, or revegetation dry fraction). We found widespread long-term positive changes in grassland vegetation (32.7% of grasslands), especially in the early 2000s, but negative changes pathways were most common before the year 2000. We found little association between changes in green vegetation and meteorological conditions, and varied relationships with livestock populations. However, we also found strong spatial heterogeneity in vegetation dynamics among neighboring fields and pastures, demonstrating capability of our approach for grassland management at local levels. Our results provide a detailed assessment of grassland vegetation change in the Caucasus Ecoregion, and present an approach to map changes in grasslands even where availability of Landsat data is limited.
File: 1-s2.0-S2666017221000225-main.pdf
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.
File: Martinuzzi-et-al_2021_HF-Argentina.pdf
Every year, wildfires destroy thousands of buildings in the United States, especially in the rapidly growing wildland-urban interface, where homes and wildland vegetation meet or intermingle. After a wildfire there is a window of opportunity for residents and public agencies to re-shape patterns of development, and avoid development in locations that are inherently at higher risk of wildfire destruction. We examined 28 of the most destructive wildfires in California, the state where most buildings are destroyed by wildfires, to evaluate whether locations of rebuilt and newly constructed buildings were adaptive (i.e., if building occurred in lower risk areas). In total, these fires burned 7,075 buildings from 1970 to 2009. We found minimal evidence for adaptation both in the number and placement of buildings post-fire. Rebuilding was common: 58% of the destroyed buildings were rebuilt within three to six years, and 94% within thirteen to twenty-five years after the fire. Similarly, we found minimal trends toward lower risk areas in the placement of 2,793 rebuilt and 23,404 newly constructed buildings over the course of 13–25 yr. In fact, long-term data revealed that relative risk of new construction either did not change significantly over time or increased. A destructive wildfire could provide an opportunity to assess and change building practices, yet our results show that such change is largely not occurring. As wildfires increasingly threaten communities, this lack of change could result in growing rates of destruction and loss of life.
File: Kramer_etal_2021_LUP.pdf
