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 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 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 Nest Distribution of Four Priority Raptor Species In Colorado

2021 ◽  
Author(s):  
Kevin Aagaard ◽  
Reesa Yale Conrey ◽  
James H. Gammonley
Keyword(s):  
Linear Models ◽  
Predictive Accuracy ◽  
Spatial Scales ◽  
Environmental Pollutants ◽  
Habitat Associations ◽  
Bald Eagle ◽  
Habitat Modification ◽  
Haliaeetus Leucocephalus ◽  
Distribution Models ◽  
Nest Distribution

ABSTRACT Raptors face threats such as habitat modification, climate change, and environmental pollutants in many parts of the western USA, where rapid human population growth exacerbates such pressures. However, information about distribution of raptor nests at broad spatial scales that could inform conservation efforts is lacking. To provide a contemporary estimate of nest distribution of four raptor species of special conservation concern (Bald Eagle [Haliaeetus leucocephalus], Ferruginous Hawk [Buteo regalis], Golden Eagle [Aquila chrysaetos], and Prairie Falcon [Falco mexicanus]) throughout Colorado, we used a statewide database of raptor nesting locations to inform species distribution models for monitoring and management efforts. We used generalized linear models to identify the relationship between nest locations and explanatory covariates relating to land cover, temperature, topography, and prey distribution. We investigated the effect of different methods for selecting the sample of locations available to raptors, comparing four selection frames: sampling from the observed locations of the target-group (i.e., other raptor nests), sampling from within a spatial buffer around observed locations, sampling from outside of the same buffer, or complete random sampling of the background locations without respect to observations. Out-of-sample validation techniques indicated strong predictive accuracy of our models. Each raptor species was best represented by a different one of the four approaches to sample available locations, refuting our expectation that models accounting for bias would perform better than those that did not. Our findings were consistent with generally understood habitat associations of these species. These models can be used to identify hot spots with high relative probability of use by breeding raptors and to inform future monitoring practices that use a standardized, stratified sampling design.

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.

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.

Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

2019 ◽  
Vol 76 (7) ◽  
pp. 2349-2361
Author(s):  
Benjamin Misiuk ◽  
Trevor Bell ◽  
Alec Aitken ◽  
Craig J Brown ◽  
Evan N Edinger
Keyword(s):  
Spatial Autocorrelation ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Accuracy Assessment ◽  
Small Scale ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Management Tools ◽  
Seabed Topography ◽  
Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

scholarly journals Application Natura 2000 Data For The Invasive Plants Spread Prediction

2015 ◽  
Vol 46 (4) ◽  
pp. 159-166 ◽  
Author(s):  
J. Pěknicová ◽  
D. Petrus ◽  
K. Berchová-Bímová
Keyword(s):  
Environmental Factors ◽  
Invasive Plants ◽  
Species Distribution ◽  
Natura 2000 ◽  
Distribution Model ◽  
Distribution Data ◽  
Habitat Types ◽  
Distribution Models ◽  
Occurrence Data ◽  
Heracleum Mantegazzianum

AbstractThe distribution of invasive plants depends on several environmental factors, e.g. on the distance from the vector of spreading, invaded community composition, land-use, etc. The species distribution models, a research tool for invasive plants spread prediction, involve the combination of environmental factors, occurrence data, and statistical approach. For the construction of the presented distribution model, the occurrence data on invasive plants (Solidagosp.,Fallopiasp.,Robinia pseudoaccacia,andHeracleum mantegazzianum) and Natura 2000 habitat types from the Protected Landscape Area Kokořínsko have been intersected in ArcGIS and statistically analyzed. The data analysis was focused on (1) verification of the accuracy of the Natura 2000 habitat map layer, and the accordance with the habitats occupied by invasive species and (2) identification of a suitable scale of intersection between the habitat and species distribution. Data suitability was evaluated for the construction of the model on local scale. Based on the data, the invaded habitat types were described and the optimal scale grid was evaluated. The results show the suitability of Natura 2000 habitat types for modelling, however more input data (e.g. on soil types, elevation) are needed.

scholarly journals Scaling up ideals to freedom: are densities of red knots across western Europe consistent with ideal free distribution?

2011 ◽  
Vol 278 (1719) ◽  
pp. 2728-2736 ◽  
Author(s):  
Gwenaël Quaintenne ◽  
Jan A. van Gils ◽  
Pierrick Bocher ◽  
Anne Dekinga ◽  
Theunis Piersma
Keyword(s):  
Uniform Distribution ◽  
Western Europe ◽  
Ideal Free Distribution ◽  
Scale Up ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Free Distribution ◽  
Wintering Areas ◽  
Wintering Area ◽  
Suitable Habitats

Local studies have shown that the distribution of red knots Calidris canutus across intertidal mudflats is consistent with the predictions of an ideal distribution, but not a free distribution. Here, we scale up the study of feeding distributions to their entire wintering area in western Europe. Densities of red knots were compared among seven wintering sites in The Netherlands, UK and France, where the available mollusc food stocks were also measured and from where diets were known. We tested between three different distribution models that respectively assumed (i) a uniform distribution of red knots over all areas, (ii) a uniform distribution across all suitable habitat (based on threshold densities of harvestable mollusc prey), and (iii) an ideal and free distribution (IFD) across all suitable habitats. Red knots were not homogeneously distributed across the different European wintering areas, also not when considering suitable habitats only. Their distribution was best explained by the IFD model, suggesting that the birds are exposed to interference and have good knowledge about their resource landscape at the spatial scale of NW Europe, and that the costs of movement between estuaries, at least when averaged over a whole winter, are negligible.

scholarly journals The modeled distribution of corals and sponges surrounding the Salas y Gómez and Nazca ridges with implications for high seas conservation

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11972
Author(s):  
Samuel Georgian ◽  
Lance Morgan ◽  
Daniel Wagner
Keyword(s):  
Suitable Habitat ◽  
Distribution Models ◽  
Seafloor Topography ◽  
Saturation State ◽  
Direct Observations ◽  
Rich Diversity ◽  
Spatial Management ◽  
Phosphate Silicate ◽  
Glass Sponges ◽  
Suitable Habitats

The Salas y Gómez and Nazca ridges are two adjacent seamount chains off the west coast of South America that collectively contain more than 110 seamounts. The ridges support an exceptionally rich diversity of benthic and pelagic communities, with the highest level of endemism found in any marine environment. Despite some historical fishing in the region, the seamounts are relatively pristine and represent an excellent conservation opportunity to protect a global biodiversity hotspot before it is degraded. One obstacle to effective spatial management of the ridges is the scarcity of direct observations in deeper waters throughout the region and an accompanying understanding of the distribution of key taxa. Species distribution models are increasingly used tools to quantify the distributions of species in data-poor environments. Here, we focused on modeling the distribution of demosponges, glass sponges, and stony corals, three foundation taxa that support large assemblages of associated fauna through the creation of complex habitat structures. Models were constructed at a 1 km2 resolution using presence and pseudoabsence data, dissolved oxygen, nitrate, phosphate, silicate, aragonite saturation state, and several measures of seafloor topography. Highly suitable habitat for each taxa was predicted to occur throughout the Salas y Gómez and Nazca ridges, with the most suitable habitat occurring in small patches on large terrain features such as seamounts, guyots, ridges, and escarpments. Determining the spatial distribution of these three taxa is a critical first step towards supporting the improved spatial management of the region. While the total area of highly suitable habitat was small, our results showed that nearly all of the seamounts in this region provide suitable habitats for deep-water corals and sponges and should therefore be protected from exploitation using the best available conservation measures.

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