scholarly journals Incorporating Shrub and Snag Specific LiDAR Data into GAP Wildlife Models

2015 ◽  
Vol 6 (2) ◽  
pp. 437-447
Author(s):  
Teresa J Lorenz ◽  
Kerri T Vierling ◽  
Jody Vogeler ◽  
Jeffrey Lonneker ◽  
Jocelyn Aycrigg
Keyword(s):  
Land Cover ◽  
Spatial Scales ◽  
Coniferous Forest ◽  
Model Performance ◽  
Lidar Data ◽  
Gap Model ◽  
Distribution Models ◽  
Classification Rate ◽  
3 Dimensional ◽  
Gap Models

Abstract The U.S. Geological Survey’s Gap Analysis Program (hereafter, GAP) is a nationally based program that uses land cover, vertebrate distributions, and land ownership to identify locations where gaps in conservation coverage exist, and GAP products are commonly used by government agencies, nongovernmental organizations, and private citizens. The GAP land-cover designations are based on satellite-derived data, and although these data are widely available, these data do not capture the 3-dimensional vegetation architecture that may be important in describing vertebrate distributions. To date, no studies have examined how the inclusion of snag- or shrub-specific Light Detection and Ranging (LiDAR) data might influence GAP model performance. The objectives of this paper were 1) to assess the performance of the National GAP models and Northwest GAP models with independently collected field data, and 2) to assess whether the inclusion of 3-dimensional vegetation data from LiDAR improved the performance of National GAP and Northwest GAP models. We included only two parameters from the LiDAR data: presence or absence of shrubs and presence or absence of snags ≥25 cm diameter at breast height. We surveyed for birds at>150 points in a 20,000-ha coniferous forest in northern Idaho and used data for eight shrub- and cavity-nesting species for validation purposes. On a guild level, National GAP models performed only marginally better than Northwest GAP models in correct classification rate, and LiDAR data did not improve vertebrate distribution models. At the scale used in this study, GAP models had poor predictive power and this is important for managers interested in using GAP models for species distributions at scales similar to ours, such as a small park or preserve <200 km2 in size. Additionally, because the inclusion of LiDAR data did not consistently affect the performance of GAP models, future studies might consider whether LiDAR data affect GAP model performance by examining 1) different spatial scales, 2) different LiDAR metrics, and/or 3) species-specific habitat relationships not currently available in GAP models.

scholarly journals MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1327-1334 ◽  
Author(s):  
W. Xu ◽  
B. Hays ◽  
R. Fayrer-Hosken ◽  
A. Presotto
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Area Under The Curve ◽  
Remotely Sensed Data ◽  
Distribution Models ◽  
African Savanna ◽  
Multi Scale ◽  
Land Cover Map ◽  
Better Than

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (<i>Loxodonta africana</i>) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

scholarly journals Predicting species distributions and community composition using satellite remote sensing predictors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Model Performance ◽  
Next Generation ◽  
Distribution Models

AbstractBiodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

scholarly journals MODELING THE DISTRIBUTION OF AFRICAN SAVANNA ELEPHANTS IN KRUGER NATIONAL PARK: AN APPLICATION OF MULTI-SCALE GLOBELAND30 DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1327-1334
Author(s):  
W. Xu ◽  
B. Hays ◽  
R. Fayrer-Hosken ◽  
A. Presotto
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Area Under The Curve ◽  
Remotely Sensed Data ◽  
Distribution Models ◽  
African Savanna ◽  
Multi Scale ◽  
Land Cover Map ◽  
Better Than

The ability of remote sensing to represent ecologically relevant features at multiple spatial scales makes it a powerful tool for studying wildlife distributions. Species of varying sizes perceive and interact with their environment at differing scales; therefore, it is important to consider the role of spatial resolution of remotely sensed data in the creation of distribution models. The release of the Globeland30 land cover classification in 2014, with its 30 m resolution, presents the opportunity to do precisely that. We created a series of Maximum Entropy distribution models for African savanna elephants (<i>Loxodonta africana</i>) using Globeland30 data analyzed at varying resolutions. We compared these with similarly re-sampled models created from the European Space Agency’s Global Land Cover Map (Globcover). These data, in combination with GIS layers of topography and distance to roads, human activity, and water, as well as elephant GPS collar data, were used with MaxEnt software to produce the final distribution models. The AUC (Area Under the Curve) scores indicated that the models created from 600 m data performed better than other spatial resolutions and that the Globeland30 models generally performed better than the Globcover models. Additionally, elevation and distance to rivers seemed to be the most important variables in our models. Our results demonstrate that Globeland30 is a valid alternative to the well-established Globcover for creating wildlife distribution models. It may even be superior for applications which require higher spatial resolution and less nuanced classifications.

scholarly journals Balancing conservation priorities for grassland and forest specialist bird communities in agriculturally dominated landscapes

2021 ◽  
Author(s):  
Devin R de Zwaan ◽  
Niloofar Alavi ◽  
Greg W Mitchell ◽  
David R Lapen ◽  
Jason Duffe ◽  
...  
Keyword(s):  
Species Diversity ◽  
Land Cover ◽  
Spatial Scales ◽  
Grassland Birds ◽  
Bird Communities ◽  
Habitat Associations ◽  
Forest Recovery ◽  
Conservation Priorities ◽  
Distribution Models ◽  
Row Crops

Effective conservation planning often requires difficult decisions when at-risk species inhabit economically valuable landscapes or if the needs of multiple threatened species do not align. In the agriculture-dominated landscape of eastern Ontario and southwestern Quebec, Canada, conflicting habitat requirements exist between threatened grassland birds benefiting from certain agriculture practices and those of a diverse woodland bird community dependent on forest recovery. Using multi-scale species distribution models with Breeding Bird Survey (BBS) data, we assessed habitat suitability for 8 threatened grassland and forest specialists within this region. We also identified landscapes that jointly maximize occurrence of the 8 focal species and diversity of the overall grassland and forest communities. Influential habitat associations differed among species at the territory (200m radius) and landscape level (1km), highlighting the importance of considering multiple spatial scales. Species diversity was maximized when forest or grassland/pasture cover approached 40-50%, indicating a positive response to land cover heterogeneity. We identified species diversity hotspots near Lake Huron, as well as along the shore and southeast of the St. Lawrence River. These areas represent mosaic landscapes, balancing forest patches, wetland, grassland/pasture, and row crops such as corn, soybean, and cereals. Despite drastic landscape changes associated with agroecosystems, we demonstrate that targeted habitat protection and enhancement that prioritizes land cover diversity can maximize protection of bird communities with directly contrasting needs. We highlight multiple pathways to achieve this balance, including forest retention or separating row crops with hedgerows and wooded fence-lines, improving flexibility in conservation approaches.

Assessing the temporal transferability of raptor distribution models: Implications for conservation

2017 ◽  
Vol 28 (3) ◽  
pp. 375-389 ◽  
Author(s):  
LUIS TAPIA ◽  
ADRIÁN REGOS ◽  
ALBERTO GIL-CARRERA ◽  
JESÚS DOMÍNGUEZ
Keyword(s):  
Land Cover ◽  
Predictive Accuracy ◽  
Spatial Scales ◽  
Bird Conservation ◽  
Distribution Models ◽  
Occurrence Data ◽  
Montagu’S Harrier ◽  
Suitable Habitats ◽  
Monitoring Protocols ◽  
Montagu's Harrier

SummaryThe aim of this study was to assess the temporal transferability of species distribution models (SDMs) and their potential implications for bird conservation. We quantified the loss and fragmentation of Montagu’s Harrier Circus pygargus and Common Kestrel Falco tinnunculus habitats over 13 years (2001–2014) in a highly dynamic landscape in north-western Spain. For this purpose, priority habitats for the target species were modelled at four different spatial scales using an ensemble forecasting framework. To explore the temporal transferability of our ensemble predictions, the models were back-projected to the land cover conditions in 2001 and evaluated using historical occurrence data. In addition, models calibrated with historical data were projected to the land cover conditions in 2014 and evaluated using updated occurrence data. Changes in availability and connectivity of suitable habitats between both years were estimated at four spatial scales from a set of widely-used indicators. SDMs showed a good predictive accuracy but with limited temporal transferability due to changes in the species-habitat relationships between 2001 and 2014. The results showed a decrease in the avaliability of suitable habitats of 33.4% and 47.7% for Montagu’s Harrier and Common Kestrel, respectively; with the subsequent increase in their fragmentation. However, our estimates were found to be strongly dependent on the scale of analysis and model transferability. Changes in habitat availability and connectivity ranged from -48% to +54% for Montagu’s Harrier, and from +116% to +5.6% for Common Kestrel. We call for caution when using SDMs beyond the model calibration time period to guide bird conservation. This is especially important for raptors, often characterised by low population sizes and large home ranges, and particularly sensitive to unstable, highly dynamic environmental conditions. In light of these results, specific, long-standing monitoring protocols remain essential to ensure accurate modelling performance and reliable future projections.

scholarly journals Dispersal and Land Cover Contribute to Pseudorabies Virus Exposure in Invasive Wild Pigs

EcoHealth ◽  
2021 ◽  
Author(s):  
Felipe A. Hernández ◽  
Amanda N. Carr ◽  
Michael P. Milleson ◽  
Hunter R. Merrill ◽  
Michael L. Avery ◽  
...  
Keyword(s):  
Land Cover ◽  
Disease Transmission ◽  
Wildlife Conservation ◽  
Pseudorabies Virus ◽  
Spatial Scales ◽  
Information Criterion ◽  
Small Scale ◽  
Wild Pigs ◽  
Bacterial Agent ◽  
Open Canopy

AbstractWe investigated the landscape epidemiology of a globally distributed mammal, the wild pig (Sus scrofa), in Florida (U.S.), where it is considered an invasive species and reservoir to pathogens that impact the health of people, domestic animals, and wildlife. Specifically, we tested the hypothesis that two commonly cited factors in disease transmission, connectivity among populations and abundant resources, would increase the likelihood of exposure to both pseudorabies virus (PrV) and Brucella spp. (bacterial agent of brucellosis) in wild pigs across the Kissimmee Valley of Florida. Using DNA from 348 wild pigs and sera from 320 individuals at 24 sites, we employed population genetic techniques to infer individual dispersal, and an Akaike information criterion framework to compare candidate logistic regression models that incorporated both dispersal and land cover composition. Our findings suggested that recent dispersal conferred higher odds of exposure to PrV, but not Brucella spp., among wild pigs throughout the Kissimmee Valley region. Odds of exposure also increased in association with agriculture and open canopy pine, prairie, and scrub habitats, likely because of highly localized resources within those land cover types. Because the effect of open canopy on PrV exposure reversed when agricultural cover was available, we suggest that small-scale resource distribution may be more important than overall resource abundance. Our results underscore the importance of studying and managing disease dynamics through multiple processes and spatial scales, particularly for non-native pathogens that threaten wildlife conservation, economy, and public health.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Spatially Continuous Land-Cover Reconstructions Through the Holocene in Southern Sweden

Ecosystems ◽  
2021 ◽  
Author(s):  
Robert O’Dwyer ◽  
Laurent Marquer ◽  
Anna-Kari Trondman ◽  
Anna Maria Jönsson
Keyword(s):  
Land Cover ◽  
Regional Scale ◽  
Coniferous Forest ◽  
Reconstruction Algorithm ◽  
Fossil Pollen ◽  
Conifer Forest ◽  
Southern Sweden ◽  
The Holocene ◽  
Simple Kriging ◽  
Pollen Records

AbstractClimate change and human activities influence the development of ecosystems, with human demand of ecosystem services altering both land use and land cover. Fossil pollen records provide time series of vegetation characteristics, and the aim of this study was to create spatially continuous reconstructions of land cover through the Holocene in southern Sweden. The Landscape Reconstruction Algorithm (LRA) was applied to obtain quantitative reconstructions of pollen-based vegetation cover at local scales, accounting for pollen production, dispersal, and deposition mechanisms. Pollen-based local vegetation estimates were produced from 41 fossil pollen records available for the region. A comparison of 17 interpolation methods was made and evaluated by comparing with current land cover. Simple kriging with cokriging using elevation was selected to interpolate the local characteristics of past land cover, to generate more detailed reconstructions of trends and degree of variability in time and space than previous studies based on pollen data representing the regional scale. Since the Mesolithic, two main processes have acted to reshape the land cover of southern Sweden, originally mostly covered by broad-leaved forests. The natural distribution limit of coniferous forest has moved southward during periods with colder climate and retracted northward during warmer periods, and human expansion in the area and agrotechnological developments has led to a gradually more open landscape, reaching maximum openness at the beginning of the 20th century. The recent intensification of agriculture has led to abandonment of less fertile agricultural fields and afforestation with conifer forest.

scholarly journals Evaluation of numerical models by FerryBox and fixed platform in situ data in the southern North Sea

Ocean Science ◽  
2015 ◽  
Vol 11 (6) ◽  
pp. 879-896 ◽  
Author(s):  
M. Haller ◽  
F. Janssen ◽  
J. Siddorn ◽  
W. Petersen ◽  
S. Dick
Keyword(s):  
North Sea ◽  
High Frequency ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Eastern Coast ◽  
Southern North Sea ◽  
In Situ Data ◽  
Oyster Ground ◽  
Study Results

Abstract. For understanding and forecasting of hydrodynamics in coastal regions, numerical models have served as an important tool for many years. In order to assess the model performance, we compared simulations to observational data of water temperature and salinity. Observations were available from FerryBox transects in the southern North Sea and, additionally, from a fixed platform of the MARNET network. More detailed analyses have been made at three different stations, located off the English eastern coast, at the Oyster Ground and in the German Bight. FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface measurements along selected tracks on a regular basis. The results of two operational hydrodynamic models have been evaluated for two different time periods: BSHcmod v4 (January 2009 to April 2012) and FOAM AMM7 NEMO (April 2011 to April 2012). While they adequately simulate temperature, both models underestimate salinity, especially near the coast in the southern North Sea. Statistical errors differ between the two models and between the measured parameters. The root mean square error (RMSE) of water temperatures amounts to 0.72 °C (BSHcmod v4) and 0.44 °C (AMM7), while for salinity the performance of BSHcmod is slightly better (0.68 compared to 1.1). The study results reveal weaknesses in both models, in terms of variability, absolute levels and limited spatial resolution. Simulation of the transition zone between the coasts and the open sea is still a demanding task for operational modelling. Thus, FerryBox data, combined with other observations with differing temporal and spatial scales, can serve as an invaluable tool not only for model evaluation, but also for model optimization by assimilation of such high-frequency observations.

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