Habitat use by Pileated Woodpeckers at two spatial scales in eastern Canada

To study the multiple spatial scale pattern of habitat use by Pileated Woodpeckers (Dryocopus pileatus L.), we compared the vegetation characteristics at used sites with those at unused sites at macrohabitat (154 ha radius plots) and microhabitat (ca. 0.04 ha radius plots) scales in the La Mauricie region of Québec. Used macrohabitats were those in which woodpeckers were detected at playback stations. Used microhabitats corresponded to sites showing signs of foraging (i.e., excavated cavities). Pileated Woodpeckers responded to vegetation structure at both scales sampled. Macrohabitats used by woodpeckers had relatively low percentages of mixed shade-intolerant hardwood stands, coniferous stands, and stands of relatively low stem density and tree height, but included high percentages of 51- to 90-year-old stands. Microhabitats used by woodpeckers were characterized most strongly by a high density of large snags but also by a high density of small-diameter snags and a low density of shade-intolerant hardwood trees and coniferous trees [Formula: see text]30 cm diameter at breast height. The concordance between the observed and predicted use of micro- and macro-habitat plots was independent of spatial scale (logistic regression, χ20.05,1 = 1.8, P = 0.18, n = 429), suggesting that habitats use by woodpeckers was influenced to a similar extent by habitat features measured at both spatial scales. Because Pileated Woodpeckers are likely to require specific habitat features at different spatial scales, forest-management guidelines intended to conserve this umbrella species should consider a multiple spatial scales approach.

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The turtle is in the details: microhabitat choice by Testudo hermanni is based on microscale plant distribution

Animal Biology ◽

10.1163/157075511x584209 ◽

2011 ◽

Vol 61 (3) ◽

pp. 249-261 ◽

Cited By ~ 7

Author(s):

Silvia Del Vecchio ◽

Lorenzo Rugiero ◽

Luca Luiselli ◽

Massimo Capula ◽

Russell L. Burke

Keyword(s):

Habitat Use ◽

Habitat Management ◽

Spatial Scales ◽

Central Italy ◽

Community Analysis ◽

Bare Soil ◽

Plant Distribution ◽

Microhabitat Use ◽

Habitat Features ◽

Testudo Hermanni

AbstractAlthough research on habitat use and habitat selection is essential for understanding population ecology and behavior, most such zoological studies have used only general habitat categories describing main habitat features instead of using modern plant ecological approaches. Here, we analyze Testudo hermanni microhabitat use at a coastal Mediterranean site in central Italy by modeling tortoise presence/absence at three spatial scales, using a logistic regression design and quantitative vegetation and plant community analysis to reveal correlates of tortoise habitat use on a fine scale. Our analyses showed that only a few plant species among the many present, and these on a very small spatial scale, are important determinants of tortoise presence and site selection. We also find that tortoises chose a paradoxical combination of high levels of bare soil and high total vegetation cover. This suggests that these tortoises are selecting small patches of habitat in a matrix of less desirable habitat. Our findings also have important implications for habitat management, in that increasing the number of habitat patches containing the few significant plants is likely to be desirable, whereas increasing the size of such patches is probably less relevant.

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Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales

Ecological Processes ◽

10.1186/s13717-021-00323-3 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Tahir Ali Rather ◽

Sharad Kumar ◽

Jamal Ahmad Khan

Keyword(s):

Machine Learning ◽

Spatial Scale ◽

Habitat Suitability ◽

Learning Algorithm ◽

Spatial Scales ◽

Buffer Zone ◽

Human Populations ◽

Land Use Patterns ◽

Habitat Features ◽

Sloth Bear

Abstract Background Habitat resources occur across the range of spatial scales in the environment. The environmental resources are characterized by upper and lower limits, which define organisms’ distribution in their communities. Animals respond to these resources at the optimal spatial scale. Therefore, multi-scale assessments are critical to identifying the correct spatial scale at which habitat resources are most influential in determining the species-habitat relationships. This study used a machine learning algorithm random forest (RF), to evaluate the scale-dependent habitat selection of sloth bears (Melursus ursinus) in and around Bandhavgarh Tiger Reserve, Madhya Pradesh, India. Results We used 155 spatially rarified occurrences out of 248 occurrence records of sloth bears obtained from camera trap captures (n = 36) and scats located (n = 212) in the field. We calculated focal statistics for 13 habitat variables across ten spatial scales surrounding each presence-absence record of sloth bears. Large (> 5000 m) and small (1000–2000 m) spatial scales were the most dominant scales at which sloth bears perceived the habitat features. Among the habitat covariates, farmlands and degraded forests were the essential patches associated with sloth bear occurrences, followed by sal and dry deciduous forests. The final habitat suitability model was highly accurate and had a very low out-of-bag (OOB) error rate. The high accuracy rate was also obtained using alternate validation matrices. Conclusions Human-dominated landscapes are characterized by expanding human populations, changing land-use patterns, and increasing habitat fragmentation. Farmland and degraded habitats constitute ~ 40% of the landform in the buffer zone of the reserve. One of the management implications may be identifying the highly suitable bear habitats in human-modified landscapes and integrating them with the existing conservation landscapes.

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Fractal analysis measures habitat use at different spatial scales: an example with American marten

Canadian Journal of Zoology ◽

10.1139/z04-167 ◽

2004 ◽

Vol 82 (11) ◽

pp. 1738-1747 ◽

Cited By ~ 67

Author(s):

Vilis O Nams ◽

Maryse Bourgeois

Keyword(s):

Fractal Dimension ◽

Habitat Use ◽

Spatial Scale ◽

Fractal Analysis ◽

Spatial Scales ◽

Habitat Type ◽

Movement Patterns ◽

American Marten ◽

Additional Information ◽

Natural Break

Habitat selection is traditionally assessed by how much time the animal spends in each habitat type; however, one can obtain additional information by analysing the structure of the movement paths. We followed and mapped snow tracks of American marten, Martes americana (Turton, 1806). The new method used to test the movement paths for deviations from a correlated random walk model show that these paths fail the test. This has led to an analysis of fractal dimension versus spatial scale, which showed a natural break in fractal dimension at a scale of approximately 3.5 m, suggesting that marten displayed different responses to their microenvironment in two regions of spatial scale. Marten travel was more direct at scales <3.5 m than at scales >3.5 m. Path tortuousity was affected by habitats at smaller scales but not at larger scales, indicating different responses by marten to their environment at these two ranges of scale. Multiple regression identified canopy closure and presence of conifer in the understory as variables that affect movement patterns at the 1- to 3.5-m scale. Fractal analysis of movement patterns provides a unique approach to examining habitat use, as well as a means to identify the spatial scales at which an animal responds to its habitat.

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

Remote Sensing ◽

10.3390/rs13122355 ◽

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.

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Deriving Tree Size Distributions of Tropical Forests from Lidar

Remote Sensing ◽

10.3390/rs13010131 ◽

2021 ◽

Vol 13 (1) ◽

pp. 131

Author(s):

Franziska Taubert ◽

Rico Fischer ◽

Nikolai Knapp ◽

Andreas Huth

Keyword(s):

Size Distribution ◽

Spatial Scales ◽

Basal Area ◽

Tree Height ◽

Tree Size ◽

Diameter Distribution ◽

Forest Inventories ◽

Allometric Relations ◽

Lidar Measurements ◽

Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

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Tree height integrated into pantropical forest biomass estimates

Biogeosciences ◽

10.5194/bg-9-3381-2012 ◽

2012 ◽

Vol 9 (8) ◽

pp. 3381-3403 ◽

Cited By ~ 239

Author(s):

T. R. Feldpausch ◽

J. Lloyd ◽

S. L. Lewis ◽

R. J. W. Brienen ◽

M. Gloor ◽

...

Keyword(s):

East Africa ◽

Carbon Storage ◽

Stand Structure ◽

Forest Biomass ◽

Small Diameter ◽

Tree Height ◽

Total Biomass ◽

Guiana Shield ◽

Tree Biomass ◽

Continental Scale

Abstract. Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha−1 (range 6.6 to 112.4) to 8.0 Mg ha−1 (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha−1 (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km2 and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.

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The influence of spatial scale and habitat arrangement on diel patterns of habitat use by two lowland river fishes

Oecologia ◽

10.1007/s004420100750 ◽

2001 ◽

Vol 129 (4) ◽

pp. 525-533 ◽

Cited By ~ 61

Author(s):

David A. Crook ◽

Alistar I. Robertson ◽

Alison J. King ◽

Paul Humphries

Keyword(s):

Habitat Use ◽

Spatial Scale ◽

Lowland River ◽

Diel Patterns

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Characteristics of Climate Change and Extreme Weather from 1951 to 2011 in China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15112540 ◽

2018 ◽

Vol 15 (11) ◽

pp. 2540 ◽

Cited By ~ 4

Author(s):

Chunli Zhao ◽

Jianguo Chen ◽

Peng Du ◽

Hongyong Yuan

Keyword(s):

Climate Change ◽

Spatial Scale ◽

Spatial Scales ◽

Seasonal Fluctuation ◽

Extreme Weather ◽

Temperature And Precipitation ◽

Weather Variation ◽

Variation Characteristics ◽

Established Fact ◽

Temporal And Spatial

It has been demonstrated that climate change is an established fact. A good comprehension of climate and extreme weather variation characteristics on a temporal and a spatial scale is important for adaptation and response. In this work, the characteristics of temperature, precipitation, and extreme weather distribution and variation is summarized for a period of 60 years and the seasonal fluctuation of temperature and precipitation is also analyzed. The results illustrate the reduction in daily and annual temperature divergence on both temporal and spatial scales. However, the gaps remain relatively significant. Furthermore, the disparity in daily and annual precipitation are found to be increasing on both temporal and spatial scales. The findings indicate that climate change, to a certain extent, narrowed the temperature gap while widening the precipitation gap on temporal and spatial scales in China.

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Scale-dependent relationships between benthic habitat characteristics and abundances of blacklip abalone, Haliotis rubra (Leach)

Marine and Freshwater Research ◽

10.1071/mf09211 ◽

2010 ◽

Vol 61 (11) ◽

pp. 1227 ◽

Cited By ~ 6

Author(s):

Elisabeth M. A. Strain ◽

Craig R. Johnson

Keyword(s):

Spatial Scale ◽

Red Algae ◽

Positive Relationship ◽

Spatial Scales ◽

Habitat Characteristics ◽

Benthic Habitat ◽

Sessile Invertebrates ◽

Haliotis Rubra ◽

The Relationship ◽

Biotic Characteristics

Habitat characteristics can influence marine herbivore densities at a range of spatial scales. We examined the relationship between benthic habitat characteristics and adult blacklip abalone (Haliotis rubra) densities across local scales (0.0625–16 m2), at 2 depths, 4 sites and 2 locations, in Tasmania, Australia. Biotic characteristics that were highly correlated with abalone densities included cover of non-calcareous encrusting red algae (NERA), non-geniculate coralline algae (NCA), a matrix of filamentous algae and sediment, sessile invertebrates, and foliose red algae. The precision of relationships varied with spatial scale. At smaller scales (0.0625–0.25 m2), there was a positive relationship between NERA and ERA, and negative relationships between sediment matrix, sessile invertebrates and abalone densities. At the largest scale (16 m2), there was a positive relationship between NERA and abalone densities. Thus, for some biotic characteristics, the relationship between NERA and abalone densities may be scalable. There was very little variability between depths and sites; however, the optimal spatial scale differed between locations. Our results suggest a dynamic interplay between the behavioural responses of H. rubra to microhabitat and/or to abalone maintaining NERA free of algae, sediment, and sessile invertebrates. This approach could be used to describe the relationship between habitat characteristics and species densities at the optimal spatial scales.

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Spatial scale-dependent land–atmospheric methane exchanges in the northern high latitudes from 1993 to 2004

Biogeosciences ◽

10.5194/bg-11-1693-2014 ◽

2014 ◽

Vol 11 (7) ◽

pp. 1693-1704 ◽

Cited By ~ 20

Author(s):

X. Zhu ◽

Q. Zhuang ◽

X. Lu ◽

L. Song

Keyword(s):

Spatial Heterogeneity ◽

Spatial Scale ◽

Water Table ◽

Spatial Scales ◽

Grid Cell ◽

High Latitudes ◽

Atmospheric Methane ◽

Ch4 Emissions ◽

Biogeochemical Models ◽

Macro Scale

Abstract. Effects of various spatial scales of water table dynamics on land–atmospheric methane (CH4) exchanges have not yet been assessed for large regions. Here we used a coupled hydrology–biogeochemistry model to quantify daily CH4 exchanges over the pan-Arctic from 1993 to 2004 at two spatial scales of 100 km and 5 km. The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for the northern high latitudes. We found that both WTD and CH4 emissions are better simulated at a 5 km spatial resolution. By considering the spatial heterogeneity of WTD, net regional CH4 emissions at a 5 km resolution are 38.1–55.4 Tg CH4 yr−1 from 1993 to 2004, which are on average 42% larger than those simulated at a 100 km resolution using a grid-cell-mean WTD scheme. The difference in annual CH4 emissions is attributed to the increased emitting area and enhanced flux density with finer resolution for WTD. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays an important role in the 100 km estimates, while the 5 km estimates are mainly influenced by WTD. This study suggests that previous macro-scale biogeochemical models using a grid-cell-mean WTD scheme might have underestimated the regional CH4 emissions. The spatial scale-dependent effects of WTD should be considered in future quantification of regional CH4 emissions.

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