Refugia are habitats that allow organisms to persist when the environment makes persistence impossible elsewhere. The subnivium—the interface between snowpack and ground—is an important seasonal refugium that protects diverse species from extreme winter temperatures, but its future duration is uncertain with climate change. Here, we predict that subnivium duration will decrease from 126 d (2010–2014) to 110 d (2071–2100), which we have inferred using past and future duration of frozen ground with snow cover (Dsc) derived from remotely sensed datasets and climate projections. Concomitantly, duration of frozen ground without snow cover (Dfwos) at mid-latitudes is predicted to increase from 35 d to 45 d, with notable increases in the western United States, Europe, the Tibetan Plateau and Mongolia. In most areas, increasing winter temperatures were more important than precipitation for decreasing Dsc and increasing Dfwos. Thus, counter-intuitively, warming climate will cause longer Dfwos at mid-latitudes, causing functional winter cooling for subnivium-dependent organisms.
File: Zhu_etal_NatureArticles_2019.pdf
Refugia are habitats that allow organisms to persist when the environment makes persistence impossible elsewhere. The subnivium—the interface between snowpack and ground—is an important seasonal refugium that protects diverse species from extreme winter temperatures, but its future duration is uncertain with climate change. Here, we predict that subnivium duration will decrease from 126 d (2010–2014) to 110 d (2071–2100), which we have inferred using past and future duration of frozen ground with snow cover (Dsc) derived from remotely sensed datasets and climate projections. Concomitantly, duration of frozen ground without snow cover (Dfwos) at mid-latitudes is predicted to increase from 35 d to 45 d, with notable increases in the western United States, Europe, the Tibetan Plateau and Mongolia. In most areas, increasing winter temperatures were more important than precipitation for decreasing Dsc and increasing Dfwos. Thus, counter-intuitively, warming climate will cause longer Dfwos at mid-latitudes, causing functional winter cooling for subnivium-dependent organisms.
Aim: To evaluate how environment and evolutionary history interact to influence
global patterns of mammal trait diversity (a combination of 14 morphological and
life-history traits).
Location: The global terrestrial environment.
Taxon: Terrestrial mammals.
Methods: We calculated patterns of spatial turnover for mammalian traits and phylogenetic
lineages using the mean nearest taxon distance. We then used a variance
partitioning approach to establish the relative contribution of trait conservatism,
ecological adaptation and clade specific ecological preferences on global trait
turnover.
Results: We provide a global scale analysis of trait turnover across mammalian terrestrial
assemblages, which demonstrates that phylogenetic turnover by itself does
not predict trait turnover better than random expectations. Conversely, trait turnover
is consistently more strongly associated with environmental variation than
predicted by our null models. The influence of clade-specific ecological preferences,
reflected by the shared component of phylogenetic turnover and environmental
variation, was considerably higher than expectations. Although global patterns of
trait turnover are dependent on the trait under consideration, there is a consistent
association between trait turnover and environmental predictive variables, regardless
of the trait considered.
Main conclusions: Our results suggest that changes in phylogenetic composition
are not always coupled with changes in trait composition on a global scale and that
environmental conditions are strongly associated with patterns of trait composition
across species assemblages, both within and across phylogenetic clades.
File: Holt2018_jbi.13091.pdf
This is a publication uploaded with a php script
Global variation in species richness is widely recognized, but the explanation
for what drives it continues to be debated. Previous efforts have focused on a
subset of potential drivers, including evolutionary rate, evolutionary time
(maximum clade age of species restricted to a region), dispersal (migration
from one region to another), ecological factors and climatic stability. However,
no study has evaluated these competing hypotheses simultaneously at a broad
spatial scale. Here, we examine their relative contribution in determining the
richness of the most comprehensive dataset of tetrapods to our knowledge
(84% of the described species), distinguishing between the direct influences of
evolutionary rate, evolutionary time and dispersal, and the indirect influences
of ecological factors and climatic stability through their effect on direct factors.
We found that evolutionary time exerted a primary influence on species richness,
with evolutionary rate being of secondary importance. By contrast,
dispersal did not significantly affect richness patterns. Ecological and climatic
stability factors influenced species richness indirectly by modifying evolutionary
time (i.e. persistence time) and rate. Overall, our findings suggest that
global heterogeneity in tetrapod richness is explained primarily by the
length of time species have had to diversify.
File: Marin-et-al.-2018.pdf
This is a publication uploaded with a php script
Wars have major economic, political and human implications, and they can strongly affect environment and land
use, not only during the conflicts, but also afterwards. However, data on the land use effects of wars is sparse,
especially for World War II, the largest war in history. Our goal was to quantify and understand the time-lagged
land use effects of WWII in Romania, by applying Structure from Motion technology to 1960s Corona spy satellite
photography. We quantified forest cutting across Romania from 1955 to 1965. This was a period when
Romania's economy recovered from the war and when Romania established close economic ties to the Soviet
Union, and when the Romanian government made reparation payments to the Soviet Union. To understand the
effects of war, we developed an accurate and fast method to orthorectify high-resolution Corona photography in
mountain areas, and rectified scanned Corona photography based on Structure from Motion technology. Our
study area of 212,000 km2 was covered by 208 Corona film strips, which we rectified with an overall average
accuracy of 14.3 m. We identified 530,000 ha of forest cuts over this time period, the rate of which is three times
higher than contemporary cutting rates. Our results highlight that the environmental and land use effects of
WWII were substantial in Romania, due to reparation payments, post-war policies regarding resource exploitation,
and technological and infrastructural development. Our research provides quantitative evidence of
how wars can cause time-lagged and long-term effects on the environment. Methodologically, we advance remote
sensing science by pioneering a new approach to orthorectify Corona photography for large areas effectively.
Corona data are available globally. Our approach facilitates the extension of the data record of space
borne observation of the earth by one to two decades earlier than what is possible with satellite datasets.
File: Nita2018_WWII_RSE.pdf
This is a publication uploaded with a php script
Mapping crop types is of great importance for assessing agricultural production, land-use patterns, an the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat’s sensor images are optimized for cropland monitoring. However, accurate mapping of crop types require frequent cloud-free images during the growing season, which are often not available, and this raises th question of whether Landsat data can be combined with data from other satellites. Here, our goal is t evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Functio (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one o two images from all cloud-free Landsat observations available for the Arlington Agricultural Researc Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used eac combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorith to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Bot the original Landsat and STARFM-predicted images were then classified with a support vector machin (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two origina Landsat images of each combination only, 2) classifying the one or two original Landsat image plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images togethe with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as th input of STARFM did not significantly improve the STARFM predictions compared to using only one, an predictions using Landsat images between July and August as input were most accurate. Including al STARFM-predicted images together with the Landsat images significantly increased average classificatio error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporatin only STARFM-predicted images for key dates decreased average classification error by 2% points (fro 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available adding STARFM predictions for key dates significantly decreased the average classification error b 4 percentage points from 30% to 26% (p < 0.05). We conclude that adding STARFM-predicted images ca be effective for improving crop-type classification when only limited Landsat observations are available but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approac to identify the optimal subsets of all STARFM predictions, which gives an alternative method of featur selection for future research.
File: Zhu2017_CropMODIS_IJAEOG.pdf
This is a publication uploaded with a php script
When timber harvesting is an important source of
local income and forest resources are declining, even
forests that are designated as protected areas may
become vulnerable. Therefore, regular monitoring
of forest disturbance is necessary to enforce the
protection of forest ecosystems. However, mapping
forest disturbance with satellite imagery can be
complicated if the majority of the harvesting is
selective logging and not clearcuts. Our goal was to
map both selective logging and clearcuts within and
outside of protected areas in Western Siberia, a region
with a highly developed timber industry. Combining
summer and winter imagery allowed us to accurately
estimate not only clearcuts, but also selective logging.
Winter Landsat images substantially improved our
classification and resulted in a highly accurate forest
disturbance map (97.5% overall accuracy and 86%
user accuracy for the rarest class, clearcuts). Selective
logging and stripcuts were the dominant disturbance
types, accounting for 96.3% of all forest disturbances,
versus 3.7% for clearcuts. The total annual forest
disturbance rate (i.e. disturbance rate for clearcuts,
stripcuts and selective logging together) was 0.53%,
but total forest disturbance within protected areas
was greater than in non-protected forest (0.66% versus
0.50%, respectively), and so was the annual rate
of selective logging (i.e. without clearcuts, 0.37%
versus 0.25%, respectively). Our results highlight that
monitoring only clearcuts without assessing selective
logging might result in significant underestimation of
forest disturbance. Also, when timber harvesting is
important for the local economy and when protected
areas have valuable timber resources that have already
been depleted elsewhere, then additional protection
may be necessary in order to maintain natural forests
within protected areas. We suggest that this is the
situation in our study area in Western Siberia right
now and is likely the situation in many other parts of
the globe as well.
File: Shchur2017_monitoring_selective_logging_with_landsat_EnvCons.pdf
This is a publication uploaded with a php script
Globally, deforestation continues, and although protected areas effectively protect forests, the
majority of forests are not in protected areas. Thus, how effective are different management regimes to
avoid deforestation in non-protected forests? We sought to assess the effectiveness of different national forestmanagement
regimes to safeguard forests outside protected areas. We compared 2000–2014 deforestation
rates across the temperate forests of 5 countries in the Himalaya (Bhutan, Nepal, China, India, and Myanmar)
of which 13% are protected. We reviewed the literature to characterize forest management regimes in each
country and conducted a quasi-experimental analysis to measure differences in deforestation of unprotected
forests among countries and states in India. Countries varied in both overarching forest-management goals
and specific tenure arrangements and policies for unprotected forests, from policies emphasizing economic
development to those focused on forest conservation. Deforestation rates differed up to 1.4% between countries,
even after accounting for local determinants of deforestation, such as human population density, market access,
and topography. The highest deforestation rates were associated with forest policies aimed at maximizing
profits and unstable tenure regimes. Deforestation in national forest-management regimes that emphasized
conservation and community management were relatively low. In India results were consistent with the
national-level results. We interpreted our results in the context of the broader literature on decentralized,
community-based natural resource management, and our findings emphasize that the type and quality of
community-based forestry programs and the degree to which they are oriented toward sustainable use rather
than economic development are important for forest protection. Our cross-national results are consistent with
results from site- and regional-scale studies that show forest-management regimes that ensure stable land
tenure and integrate local-livelihood benefits with forest conservation result in the best forest outcomes.
File: Brandt2017_forestHimalaya_ConsBio.pdf
This is a publication uploaded with a php script
The process of vegetation burning is an essential component in the dynamics of grassy arid ecosystems. An understanding of the impact of fires on various components of the arid ecosystem is required for scientific, environmental, and management tasks, and it should be assessed with a high spatial and temporal resolution. This paper presents a method and description of data to be used in such an assessment of fire dynamics. The spatiotemporal dynamics of fires in the Chernye Zemli area is described. It shows the abundance of fires, their high interannual variability, clusterization in a territory, and the dominance of large fires.
This is a publication uploaded with a php script
Maintaining habitat and its connectivity is amajor conservation goal, especially for large carnivores. Assessments
of habitat connectivity are typically based on the output of habitat suitability models to first map potential habitat,
and then identify where corridors exist. This requires separating habitat from non- habitat, thus one must
choose specific thresholds for both habitat suitability and the minimum patch size that can be occupied. The selection
of these thresholds is often arbitrary, and the effects of threshold choice on assessments of connectivity
are largely unknown. We sought to quantify howhabitat-suitability and patch-size thresholds influence connectivity
assessments for jaguars (Panthera onca) in the Sierra Gorda Biosphere Reserve in central Mexico. We
modeled potential habitat for jaguars using the species distribution modeling algorithm Maxent, and assessed
potential habitat connectivity with the landscape connectivity software Conefor Sensinode. We repeated these
analyses for 45 combinations of habitat suitability based thresholds and minimum patch sizes. Our results indicated
that the thresholds influenced connectivity assessments greatly, and different combinations of the two
thresholds yielded vastly different map configurations of suitable habitat for jaguars.We developed an approach
to identify the pair of thresholds that bestmatched the jaguar occurrence points based on the connectivity scores.
Among the combinations that we tested, a threshold of 0.3 for habitat suitability and 2 km2 for minimum patch
size produced the best fit (area under the curve=0.9). Surprisingly, we found lowsuitable habitat for jaguars in
most of the core areas of the reserve according to our best potential habitatmodel, but highly suitable areas in the
buffer zones and just outside of the reserve. We conclude that the best and most connected potential areas for
jaguar habitat are in the central eastern part of the Sierra Gorda. More broadly, landscape connectivity analyses
appears to be highly sensitive to the thresholds used to identify suitable habitat, and we recommend conducting
sensitivity analyses as introduced here to identify the optimal combination of thresholds.
File: RamirezetaljaguarBioCons2016.pdf
This is a publication uploaded with a php script
Fine-scale information about urban vegetation and social-ecological relationships
is crucial to inform both urban planning and ecological research, and high spatial resolution
imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing
have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation
cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships
of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto
Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution
(0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3)
investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental
factors. We found that 61% of the San Juan Metropolitan Area was green and that
our combination of high spatial resolution imagery and object-based classification was highly
successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition,
simple spatial pattern analysis allowed us to separate residential from non-residential vegetation
with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly
across the city. Both socioeconomic (e.g., population density, building age, detached homes)
and environmental variables (e.g., topography) were important in explaining variations in vegetation
cover in our spatial regression models. However, important socioeconomic drivers found
in cities in temperate zones, such as income and home value, were not important in San Juan.
Climatic and cultural differences between tropical and temperate cities may result in different
social-ecological relationships. Our study provides novel information for local land use
planners, highlights the value of high spatial resolution remote sensing data to advance ecological
research and urban planning in tropical cities, and emphasizes the need for more studies in
tropical cities.
File: Martinuzzi2018_VegCover_EcoApps.pdf
This is a publication uploaded with a php script
