There is ongoing debate among conservationists regarding the value of small habitat patches to sustain wild populations in farmlands. Our goal was to assess bird abundance in riparian forests differing in terms of size, configuration, landscape conditions and degradation level, to both inform the debate and to identify conservation strategies to maintain diverse agricultural landscapes. We conducted bird point-counts in 91 sites in 2016 across an agricultural valley in Chile. Using models that accounted for imperfect detection, we assessed variation in bird densities in riparian forests with different sizes and configuration, landscapes, and habitat characteristics. We found support in univariates models for our prediction that bird densities varied across riparian forest of various sizes and configuration for 10 of 16 bird species. However, when we added landscape and habitat characteristics to the model, we found that the densities of many of the birds were best explained by forest cover around their local (1 ha) and broader (50 ha) landscape combined with forests characteristics (e.g., invasive tree abundance). For example, Black-throated huet-huet and Chucao Tapaculo were positively associated with forest cover at the broader landscape (50 ha), but showed no response to number of patches, patch-size and Euclidean distance. Our results showed no evidence of negative fragmentation effect per se (i.e., after controlling for habitat area). While agricultural landscapes provide habitat for some species that use small forest patches, conservation strategies focusing on maintaining high level of forest cover and native vegetation are required to secure populations of forest affiliated species.
File: Rojas_et_all_BioConservation_2024_Riparian_forest_patches.pdf
Protected areas are cornerstones of conservation efforts worldwide. However, protected areas do not act in isolation because they are connected with surrounding, unprotected lands. Few studies have evaluated the effects of protected areas on wildlife populations inhabiting private lands in the surrounding landscapes. The lowland tapir Tapirus terrestris is the largest terrestrial mammal of the Neotropics and is categorized as Vulnerable on the IUCN Red List. It is necessary to understand the influence of landscape characteristics on the tapir’s habitat use to enable effective conservation management for this species. Our objectives were to () determine the potential distribution of the lowland tapir’s habitat in the Southern Yungas of Argentina, and () evaluate the role of protected areas and other covariates on tapir habitat use in adjacent private lands. We used records of lowland tapirs to model the species’ potential distribution and determined habitat use with occupancy modelling. Based on the covariates found to be significant in our models, we constructed predictive maps of probability of habitat use and assessed the area of potential habitat remaining for the species. Probability of habitat use was higher in the vicinity of two national parks and small households than further away from them. We found that in % of the lowland tapir’s potential distribution the probability of habitat use is high (..). These areas are near the three national parks in the study area. The probability of detecting lowland tapirs increased with distance to roads. We conclude that national parks play a key role in the persistence of lowland tapir populations on adjacent private lands.
File: Rivera-et-al-2021_National-Parks-influence-habitat-use-of-lowland-tapirs_Southern-Yungas-of-Argentina.pdf
Understandingof the vulnerabilityof populationsexposedto wildfires is limited.We usedan index from the U.S.Centersfor DiseaseControl and Preventionto assessthe socialvulnerabilityof populationsexposedto wildfirefrom 2000–2021in California,Oregon,and Washington,whichaccountedfor 90%of exposures in the westernUnitedStates. The numberof peopleexposedto fire from 2000–2010to 2011–2021increasedsubstantially, withthe largest increase,nearly250%,for peoplewithhighsocialvulnerability. In Oregonand Washington,a higherpercentageof exposedpeoplewere highlyvulnerable (>40%)thanin California(~8%).Increasedsocialvulner-abilityof populationsin burnedareas was the primarycontributorto increasedexposure of the highlyvulner-able in California,whereas encroachmentof wildfires on vulnerable populationswas the primarycontributorinOregonand Washington.Our resultsemphasizethe importanceof integrating the vulnerabilityof at-riskpop-ulationsin wildfire mitigation and adaptation plans.
File: sciadv.adh4615.pdf
The Wildland-Urban Interface (WUI) is the area where natural vegetation is close to housing and area of concern due to various negative consequences for humans and the environment including fire ignitions, landscape fragmentation and human-wildlife interactions. The WUI is a global phenomenon, and widespread in many countries but long-term WUI dynamics and the main factors causing WUI growth are unknown. Our goal was to assess WUI changes in the Polish Carpathians since the mid-19th century, based on high-resolution spatial data for 1860s, 1970s and 2013. We found that WUI covered already 30% of the study area in the 1860s but grew to cover nearly half by 2013, especially at lower elevations. Detailed analysis of WUI determinants confirmed the areas closer to regional administrative centres or located on steep slopes were more WUI-prone. Tourist trail density also fostered WUI occurrence. We conclude that in Central Europe, with a long history of human settlements and agricultural activities, WUI has been a persistent landscape feature for centuries, but increased in area in recent decades due to widespread abandonment of agricultural land combined with development of new residential areas.
File: Kaim_AppGeog_2024.pdf
Socioeconomic shocks can cause regime shifts in land use, but even during shocks, and when land use change is widespread, some areas persist in their land use. The question is what makes these areas more resistant. Our research goal was to find out what explains where arable farming persisted despite a major socioeconomic shock of forced post-war displacements. Our study area were 291 villages in the Polish Carpathians where abandonment due to the forced displacement of the Ukrainian population after WWII was widespread. We compared prewar arable land with 1990 CORINE Land Cover data to quantify land-use change throughout the socialist period. We applied logistic regression with economically relevant environmental and access-related variables, and assessed the explanatory power of our models and relative importance of determinants. Forty years after forced displacements, arable farming persisted only in a small portion of what had been farmed in the 1940s (16 %), while the majority of former arable land converted to forests (54 %) or grasslands (22 %). Arable farming persisted mainly in areas with high accessibility that had oak-hornbeam forest as potential natural vegetation, on less steep slopes, and at lower elevations. Our models predicting agricultural abandonment leading to reforestation performed well (R2 = 0.57), but our model of persistent agriculture had low explanatory power (R2 = 0.26) as did models of conversion to grassland (R2 = 0.24). We therefore conclude that agricultural persistence is driven by different factors than agricultural land abandonment. In the long term, after arable farming ceases, areas can either be completely abandoned or convert to less intensive grassland use. These long-term changes have strong effects on biodiversity and ecosystem services, but are not well predicted by environmental and access-related determinants. Our findings can help to develop strategies and policies for areas affected by agricultural land abandonment caused by depopulation, and other socioeconomic shocks, and highlight the need to understand not only why arable land is abandoned, but also what determines its long-term fate.
File: Kaim_LandUsePolicy_2023.pdf
The wildland – urban interface (WUI) is the zone where human settlements are in or near areas of fire-prone wildland vegetation. The WUI is widespread and expanding, with detrimental consequences to human lives, property, and neighboring ecosystems. While the WUI has been mapped in many regions, Europe does not have a high resolution WUI map to date. Moreover, while most WUI research has been focused on quantifying spatial and temporal patterns, little is known about the relationship between the WUI and the socioeconomic conditions that drive its formation. Here, we present the first high-resolution map of the European WUI and provide the first macro-scale analysis of the relationship between the WUI and some of its potential drivers. We found that the WUI covers about 7.4 % of Europe, but its extent varies considerably both across and within countries, with subnational WUI cover varying from nearly zero to almost 90 %. WUI cover is significantly related to socioeconomic variables such as GDP per capita, the proportion of the population above 65 years old, population density, road density, and the proportion of protected areas, but these effects are complex and interactive. This suggests that WUI drivers are likely to differ across and within countries, and hints about the importance of both top-down and local socioeconomic processes in driving the WUI. Our new WUI map can facilitate local as well as regional-scale wildfire risk and ecological assessments that inform policy and management decisions aimed at reducing the detrimental outcomes of the WUI in Europe.
File: BarMassada_LUP_2023.pdf
Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2and exposed more than 8500 homes from 1990 to 2019.Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed.. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC=0.88 andr=0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000(exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk com-pared with change in observed exposure. Increases in risk between the 1990sand 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000sand 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.
File: Ecosphere-2023-Hawbaker-Changes-in-wildfire-occurrence-and-risk-to-homes-from-1990-through-2019-in-the-Southern-Rocky.pdf
Accurate maps of gains in tree cover are necessary to quantify carbon storage, wildlife habitat, and land use changes. Satellite-based mapping of emerging smallholder woodlots in heterogeneous landscapes of sub-Saharan Africa is challenging. Our goal was to evaluate the use of time series to detect and map small woodlots (<1 ha) in Tanzania. We distinguished woodlots from other land cover types by woodlots’ distinct multi-year spectral time series. Woodlots exhibit greening from planting to maturity followed by browning at harvest. We compared two time series approaches: 1) a linear model of Tasseled Cap Wetness (TCW) and other indices, and 2) LandTrendr temporal segmentation metrics. The approaches had equivalent woodlot detection accuracy, but LandTrendr segments had lower accuracy for characterizing woodlot age. We tested the effect of the following factors on woodlot detection and mapping accuracy: the length of the time series (2009–2019), frequency of observations (all Landsat vs. only Landsat-8), spatial resolution (30-m Landsat vs. 10-m Sentinel-2), and woodlot age and size. Woodlot mapping accuracies were higher with longer time series (54% at 3-yrs vs 77% at 7-yrs). The accuracies also improved with more observations, especially when the time series was short (3-yrs Landsat-8 only: 54% vs. all-Landsat: 64%, p-value <0.001). Sentinel-2’s higher spatial resolution minimized commission errors even for short time series. Finally, less than half of young and small (<0.4 ha) woodlots were detected, suggesting considerable omission errors in our and other woodlot maps. Our results suggest that the accurate detection of woodlots is possible by analyzing multi-year time series of Landsat and Sentinel-2 data. Given the region’s woodlot boom, accurate maps are needed to better quantify woodlots’ contribution to carbon sequestration, livelihoods enhancement, and landscape management.
File: Kimambo_ScienceRS_2023.pdf
Wildfire risks to homes are increasing, especially in the wildland-urban interface (WUI), where wildland vegetation and houses are in close proximity. Notably, we found that more houses are exposed to and destroyed by grassland and shrubland fires than by forest fires in the United States. Destruction was more likely in forest fires, but they burned less WUI. The number of houses within wildfire perimeters has doubled since the 1990s because of both housing growth (47% of additionally exposed houses) and more burned area (53%). Most exposed houses were in the WUI, which grew substantially during the 2010s (2.6million new WUI houses), albeit not as rapidly as before. Any WUI growth increases wildfire risk to houses though, and more fires increase the risk to existing WUI houses.
File: Radeloff_Science_WUI_2023.pdf
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
