Landscape Habitat Characteristics Research Articles

Linking landscape habitats with prevalence of fusarium wilt disease of cashew crop in Tanzania

Epidemic of Cashew Fusarium wilt disease (CFWD) has been a continuous focal challenge in the cashew farming, in Tanzania. Limited to edaphic conditions as a major factor in its epidemic, the current study aimed to assess the habitat-disease relationship. Purposive surveys involving assessment of disease prevalence and habitat compositions were conducted across four landscapes of southeastern zone from 2019 to 2023. Findings revealed a widespread of CFWD across diversified landscapes possessing varying habitat characteristics, mainly cultivated land with mature cashew, brownish sand loamy soils, grassland or shrub vegetation, seasonal river streamlines and natural water wells. The highest disease incidence and severity were noted at Nachingwea/Masasi plain (99.28:88.34%) followed by Liwale inland plain (98.64:89.3%), Coastal zone (72.72:59.83%) and Tunduru dissected plain (62.13:54.54%). The habitat characteristics were strongly similar within the landscape (0.86-Jaccard index) except between villages of the coastal zone (0.71-Jaccard index). Across landscapes, Nachingwea/Masasi plains and the Coastal zone were strongly similar to Tunduru dissected plain (0.63—1.0-Jaccard index), but strongly dissimilar with the Liwale inland plain (0.67—0.70- Jaccard distance). Furthermore, the presence of greater than 0.5 suitability indices across landscapes were revealed, with Liwale inland plain having strongest suitability index of 0.743 followed by Coastal zone (0.681), Tunduru dissected plain (0.617) and Nachingwea/Masasi plain. Significantly, the habitats had an increase of 0.1 suitability index, and positively correlated with disease prevalence by triggering disease incidence of 13.9% and severity of 31.4%. The study for the first time revealed the presence of an association between disease prevalence and landscape habitat characteristics of southeastern, Tanzania; paving the way to inclusive thinking of habitat as one of the drivers in the prevalence of fusarium wilt disease of cashews. Further research on the genetic coevolution of Fusarium oxysporum across landscapes to strengthen disease risk management in the cashew industry is recommended.

Farmland birds occupying forest clear-cuts respond to both local and landscape features

Agricultural landscapes have changed fast in Europe, which has led to steep declines in farmland biodiversity. While this has differentially impacted “farmland species”, some of these seem to be able to use alternative man-made open habitats in forests, such as forest clear-cuts and powerline rights-of-way. We investigated the use of clear-cuts by two historically declining farmland birds, the Yellowhammer (Emberiza citrinella) and the Red-backed Shrike (Lanius collurio), to determine both local and landscape habitat characteristics of clear-cuts used for breeding. Among 101 clear-cuts visited in Scania, southern Sweden, 27% were occupied by Yellowhammers and 12% by Red-backed Shrikes. Yellowhammer occurrence in clear-cuts was positively related to local spruce cover (the planted tree species). There was also an interaction between clear-cut size and the proportion of farmland in the landscape, such that the positive effect of the proportion of farmland nearby was stronger for larger clear-cuts. Red-backed Shrike occurrence was positively related to clear-cut size and the local presence of piles of forest residues (tops and branches). The relation between occurrence and the proportion of clear-cuts in the landscape was positive for small clear-cuts and negative for larger ones. Yellowhammers and Red-backed shrikes are affected by both the local habitat structure in the clearcut and on the composition of the surrounding landscape, most likely because they use the clear-cut for nesting and the surroundings as complementary feeding habitat. To what extent clear-cuts as complementary breeding habitat to farmland may contribute to the conservation of Yellowhammers and Red-backed shrikes, as well as other farmland birds, is a promising path for future investigations.

Landscape Characteristics of Oriental Honey Buzzards Wintering in Western Part of Flores Island Based on Satellite-Tracking Data

Oriental Honey Buzzards (OHBs, Pernis ptilorhynchus) are migratory raptor that has been satellite-tracked since 2003. Some islands in Indonesia which are used for wintering habitat are Flores and Borneo. However, both islands have different characteristics of climate and land cover. The objectives of this research were to analyze the landscape characteristic of the OHBs wintering habitat in western Flores, and to subsequently compare landscape characteristic of the OHBs wintering habitat in Borneo. Landscape habitat characteristics were analyzed using Principal Component Analysis (PCA) combined with GIS and then compared to the previous study in Borneo Island. The result showed that the first of six principal components explained 79.14% and 77.59% of the observed variation in landscape characteristics of both core and edge habitats, subsequently. Habitat selection by OHBs at wintering site was influenced by the availability of thermal wind and food. Savannah was identified as the main landscape characteristic that was different between wintering habitat in Flores and Borneo. Savannah is well-known as a habitat for many species of amphibians, reptiles, and small mammals so that it can be a hunting area that provide alternative feed for OHBs.

Using landscape habitat associations to prioritize areas of conservation action for terrestrial birds.

Predicting species distributions has long been a valuable tool to plan and focus efforts for biodiversity conservation, particularly because such an approach allows researchers and managers to evaluate species distribution changes in response to various threats. Utilizing data from a long-term monitoring program and land cover data sets, we modeled the probability of occupancy and colonization for 38 bird Species of Greatest Conservation Need (SGCN) in the robust design occupancy modeling framework, and used results from the best models to predict occupancy and colonization on the Iowa landscape. Bird surveys were conducted at 292 properties from April to October, 2006–2014. We calculated landscape habitat characteristics at multiple spatial scales surrounding each of our surveyed properties to be used in our models and then used kriging in ArcGIS to create predictive maps of species distributions. We validated models with data from 2013 using the area under the receiver operating characteristic curve (AUC). Probability of occupancy ranged from 0.001 (SE < 0.001) to 0.995 (SE = 0.004) for all species and probability of colonization ranged from 0.001 (SE < 0.001) to 0.999 (SE < 0.001) for all species. AUC values for predictive models ranged from 0.525–0.924 for all species, with 17 species having predictive models considered useful (AUC > 0.70). The most important predictor for occupancy of grassland birds was percentage of the landscape in grassland habitat, and the most important predictor for woodland birds was percentage of the landscape in woodland habitat. This emphasizes the need for managers to restore specific habitats on the landscape. In an era during which funding continues to decrease for conservation agencies, our approach aids in determining where to focus limited resources to best conserve bird species of conservation concern.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Current Distribution, Relative Abundance, and Landscape-Level Habitat Associations of the Cerulean Warbler (Setophaga cerulea) Along the Lower Roanoke River in North Carolina

Setophaga cerulea (Cerulean Warbler) has been inadequately monitored along the Roanoke River in North Carolina since a breeding population was discovered there in 1972. Our objectives were to estimate the Cerulean Warbler's current population size and distribution along the river, and evaluate landscape habitat characteristics in the Roanoke River Basin among areas used and unused by the same species. In May 2001 and 2011, we surveyed for singing male Cerulean Warblers, primarily by boat, along approximately 160 km of the Roanoke River from Weldon to Williamston in northeast North Carolina. We found Cerulean Warblers in three distinct groups along the Roanoke River during both survey years; however, we detected at least 32.4% fewer males in 2011 (n = 23) than in 2001 (n = 34). The landscape within 500 m of areas used by Cerulean Warblers had significanlty less crop cover, blackwater floodplain (i.e., swamp) forest, and variation in mean canopy height than unused landscapes we surveyed. These same differences existed at distances up to 1 km, but several additional dissimilarities became evident at this scale, including presence of more evergreen plantations and a greater fragmentation of the dominant forested land cover at used versus unused landscapes. We recommend continued monitoring of the Cerulean Warbler along the Roanoke River, increased habitat protection, and encourage an in-depth investigation into management strategies to sustain this population.

Forest-edge utilization by carnivores in relation to local and landscape habitat characteristics in central European farmland

Rapid changes in agricultural landscape structure and composition affect many different farmland biotas, including carnivores, which are a key element of ecosystem stability, yet little is known about their distribution and habitat use. In this study, we evaluated how habitat characteristics on two different spatial scales (local and landscape scale) affected the forest-edge utilization by small and medium-sized carnivores in fragmented central European farmland. Based on an indirect method for detecting carnivores (scent stations), we sampled 212 forest fragments of different sizes (1–7864ha) during April to May from 2006 to 2009. Our results indicate that carnivore utilization of forest-edge habitats was driven by landscape rather than local characteristics even though the overall extent of explained variation was small. The most important factors that determined response of the carnivore community were the area of farmland and that of urban land on a landscape scale. The corridor connectivity between small forest fragments and other spatial elements played a crucial role in the occurrence of red fox. Our results suggest that comprehensive studies on multi-species carnivore assemblage using scent station might be useful in evaluating species-specific response to habitat characteristics, especially if large numbers of stations visited by carnivores are available.

Temporal, Spatial, and Landscape Habitat Characteristics of Moose–Vehicle Collisions in Western Maine

We analyzed moose (Alces alces)–vehicle collisions (MVCs) in western Maine, USA, from 1992 to 2005 (n = 8,156) using Geographic Information Systems to identify patterns of temporal and spatial distribution and develop predictive models based on road and landscape characteristics. We used chi-square and correlation analyses to assess temporal characteristics of MVCs, K-function and kernel analyses to identify spatial clusters of MVCs, and logistic regression to relate covariates for traffic, land-cover, land-form, and relative moose abundance to probability of MVC. We evaluated candidate models using Akaike's Information Criterion, area under the receiver operating characteristic curve (AUC), and the percentage of correctly classified observations. Most (81.6%) MVCs occurred from May to October, with peak monthly frequencies in June (18.6%). Moose–vehicle collisions were clustered spatially on roads at local (0–4 km) and regional scales (22–41 km and 45–54 km), but not at intermediate scales. Traffic-related covariates predicting MVCs included traffic volume and speed limit. For each additional 500 vehicles/day, odds of a location being an MVC increased by 57%. For each 8-km/hr increase in speed limit, odds of an MVC increased by 35%. Landscape composition covariates best predicted MVCs within a 2.5-km radius of the collision site. Mean percent cover within 2.5 km of MVCs was comprised of 36% more cutover forest, 10% more coniferous forest, 5% less deciduous–mixed forest, and 10% less nonwoody wetland than for random points. For every 5% increase in percent cutover and coniferous forest within 2.5 km of the road, predicted odds of MVC increased by 36% and 19%, respectively. Landscape configuration covariates best predicted MVCs within the 5.0-km radius. Moose–vehicle collisions were associated with areas of less interspersion of cover types; for each 5% increase in an index of interspersion–juxtaposition, predicted odds of MVC decreased by 11%. Our final model attained high predictive power (AUC = 0.835) and validation accuracy (75.0%). The model also proved robust to physiographic variation, exhibiting high predictive power (AUC = 0.828) and validation accuracy (68.8%). Managers seeking to prioritize resources for reducing MVCs or predicting future areas of high MVC probability should assess land-cover composition and configuration surrounding MVC hotspots at geographic extents out to 2.5–5 km and use this information to plan expensive roadside management practices such as fencing. The importance of traffic and landscape covariates in our modeling suggests that effective management to reduce MVCs will require a complex combination of driving speed reductions and modifications to forest management along roads.

Temporal, Spatial, and Landscape Habitat Characteristics of Moose—Vehicle Collisions in Western Maine

Abstract:We analyzed moose (Alces alces)‐vehicle collisions (MVCs) in western Maine, USA, from 1992 to 2005 (n= 8,156) using Geographic Information Systems to identify patterns of temporal and spatial distribution and develop predictive models based on road and landscape characteristics. We used chi‐square and correlation analyses to assess temporal characteristics of MVCs,K‐function and kernel analyses to identify spatial clusters of MVCs, and logistic regression to relate covariates for traffic, land‐cover, land‐form, and relative moose abundance to probability of MVC. We evaluated candidate models using Akaike's Information Criterion, area under the receiver operating characteristic curve (AUC), and the percentage of correctly classified observations. Most (81.6%) MVCs occurred from May to October, with peak monthly frequencies in June (18.6%). Moose‐vehicle collisions were clustered spatially on roads at local (0–4 km) and regional scales (22–41 km and 45–54 km), but not at intermediate scales. Traffic‐related covariates predicting MVCs included traffic volume and speed limit. For each additional 500 vehicles/day, odds of a location being an MVC increased by 57%. For each 8‐km/hr increase in speed limit, odds of an MVC increased by 35%. Landscape composition covariates best predicted MVCs within a 2.5‐km radius of the collision site. Mean percent cover within 2.5 km of MVCs was comprised of 36% more cutover forest, 10% more coniferous forest, 5% less deciduous‐mixed forest, and 10% less nonwoody wetland than for random points. For every 5% increase in percent cutover and coniferous forest within 2.5 km of the road, predicted odds of MVC increased by 36% and 19%, respectively. Landscape configuration covariates best predicted MVCs within the 5.0‐km radius. Moose‐vehicle collisions were associated with areas of less interspersion of cover types; for each 5% increase in an index of interspersion‐juxtaposition, predicted odds of MVC decreased by 11%. Our final model attained high predictive power (AUC = 0.835) and validation accuracy (75.0%). The model also proved robust to physiographic variation, exhibiting high predictive power (AUC = 0.828) and validation accuracy (68.8%). Managers seeking to prioritize resources for reducing MVCs or predicting future areas of high MVC probability should assess land‐cover composition and configuration surrounding MVC hotspots at geographic extents out to 2.5–5 km and use this information to plan expensive roadside management practices such as fencing. The importance of traffic and landscape covariates in our modeling suggests that effective management to reduce MVCs will require a complex combination of driving speed reductions and modifications to forest management along roads.