Spatial Pattern Of Habitat Research Articles

Landscape patterns drive provision of nature's contributions to people by mobile species

Abstract Predicting how nature's contributions to people (NCP) vary spatially remains a challenge. For NCP provided by mobile species, it is unclear how predictions need to account for the influence of multiple habitat types that act as sources, sinks and potential distractors of the NCP‐providing species. Existing approaches that do not account for these effects may inaccurately predict outcomes in real landscapes. To move beyond these limitations, we transfer quantitative inference approaches from movement ecology to explore how spatial habitat patterns determine the negative NCP of the invasive common brushtail possum Trichosurus vulpecula in New Zealand. We used a Bayesian model to investigate how the size of, and distance from, grassland and indigenous and exotic forest patches together contribute to relative possum density (measured by capture probability across a trapping network) in a heterogeneous 11,000‐ha landscape. We found that indigenous and exotic forest area were the most important factors in determining possum density. Although capture probability declined with increasing grassland area, the addition of grassland did not improve the relative model fit above one with indigenous forest as the only habitat. We expected differences in predicted possum density at habitat boundaries, for example, due to preferential foraging at edges. We found that indigenous and exotic forests contributed to capture probability interactively, such that capture probability at the between‐habitat edge was lower than expected, given the habitat area. We also found that models allowing for non‐linear habitat effects of exotic forests or grasslands, but not indigenous forests, were significantly better at predicting possum density than simpler models. Synthesis and applications. Our novel approach for spatial prediction can be applied to any of nature's contributions to people (NCP), and extended to identify trade‐offs and synergies among multiple NCP. For example, the negative NCP of possum density trades off with multiple known positive NCP from indigenous forests, including culturally significant non‐material NCP, and material NCP produced by exotic forests. We recommend that landscape management plans to maximise these positive NCP in future scenarios also consider how the risk of possum density may dampen net NCP provision. To minimise this negative NCP, our results support trap deployment in both indigenous and exotic forest.

High-throughput sequencing and fatty acid profile analyses of the Black Amur bream (Megalobrama terminalis) reveal variation in dietary niche associated with geographic segregation.

Fish dietary niche is a core focus, and it reflects the diversity of resources, habitats, or environments occupied by a species. However, whether geographic segregation among different populations triggers dietary diversification and concomitant fish niche shift remains unknown. In the present study, we selected the Black Amur bream (Megalobrama terminalis) is a migratory fish species that plays an important role in the material transfer and energy cycling of river ecosystems, inhabiting southern China drainage with multiple geographic populations. Here, we utilized the combined analyses of 18S rDNA high-throughput sequencing in fish gut contents and fatty acid (FA) in muscle tissues to evaluate potential spatial patterns of habitat and resource use for M. terminalis in three rivers of southern China. Our results showed that prey items of the Xijiang (XR) population (Pearl River) exhibited the highest species diversity and richness among the three geographic populations. Moreover, diet composition of M. terminalis was affected by spatial differences associated with geographic segregation. Analyses of FA biomarkers indicated that the highest levels of C16:0, C18:3n-3, and C18:2n-6c were found in Wanquan (WS) population (Wanquan River). The XR population exhibited a distinct FA profile characterized by higher amounts of arachidonic acid (ARA) and docosahexaenoic acid (DHA). The Moyang (MY) population (Moyang River) acted as the linkage between WS and XR populations and consisted of middle levels of saturated FAs (SFAs) and polyunsaturated FAs (PUFAs). The XR population displayed a greater FA niche width compared with WS population. Furthermore, we observed a close positive relationship between the niche width and α-diversity indices of dietary resources for FA proflies. Our study provides valued information to develop different conservation strategies among different populations and improve fisheries management for M. terminalis and other endemic species in local rivers.

Temporal and spatial patterns of habitat of Nipponia nippon in China under the background of climate change.

Crested ibis (Nipponia nippon) is one of the endangered species in the International Union for Conservation of Nature. It is of great significance to pay attention to the changes of its suitable habitat in the context of climate change. Based on the geographical distribution data of crested ibis, the MaxEnt model was used to predict the suitable habitat of crested ibis under current scenario and future climate change. The results showed that the prediction accuracy of MaxEnt model was high, with an AUC value of 0.989. The minimum temperature of the coldest month, the mean temperature of the coldest quarter, and the mean annual rainfall were the dominant environmental factors affecting the habitat of crested ibis. Under current climate scenario, the area of moderately and highly suitable area of Chinese crested ibis was 10.65×104 km2, mainly distributed in Shaanxi, Sichuan, Hubei, Henan, and Gansu. In the future, the suitable habitat area of crested ibis would increase significantly under climate change, mainly distributed in Anhui, Chongqing, Guizhou, Jiangsu, Hunan, Shandong, Shaanxi, Jiangxi, Taiwan, Yunnan, Liaoning, and Fujian. In the SSP126 scenario from 2041 to 2060, the suitable habitat area of crested ibis would reach the maximum, being 139.53×104 km2 higher than that of the current climate scenario, accounting for 19.6% of the land area. This study could provide a basis for policy making on the conservation of crested ibis under global climate change.

Opportunistic sightings from fisheries surveys inform habitat suitability for northern bottlenose whales Hyperoodon ampullatus and sperm whales Physeter macrocephalus in Baffin Bay and Davis Strait, Canadian Arctic

Knowledge of spatial habitat patterns is critical for understanding the ecology of cetaceans and informing conservation efforts. However, this can be difficult to obtain for species that live in deep, offshore Arctic waters where they are not easily observed. We investigated habitat suitability for 2 cetacean species, northern bottlenose whale Hyperoodon ampullatus and sperm whale Physeter macrocephalus, in Baffin Bay and Davis Strait, eastern Canadian Arctic. Presence locations were obtained from a unique marine mammal sightings data set where observations were opportunistically recorded during annual government-led fisheries surveys (1999-2017). Environmental variable data were used as predictors in presence-only habitat suitability modelling in Maxent software. A total of 12 sperm whales were observed at 9 unique locations, and 282 northern bottlenose whales were observed at 66 unique locations. The best habitat suitability model for sperm whale (area under the curve [AUC] = 0.72) and for northern bottlenose whale (AUC = 0.88) indicated higher suitability for both species in the central portion of the study area; higher suitability for sperm whales was also present in the southern part of the study area. A future projections scenario using environmental data from 2021 forecasted an increase in suitability in northern regions for both species. Post-model comparisons identified significant relationships between survey effort and habitat suitability, and squid biomass and habitat suitability for both species, although the variance explained by these models was low. We discuss the importance of monitoring cetacean range expansion of temperate whales in the Arctic and how this could lead to shifts in ecosystem dynamics and increased conflict with commercial fisheries.

Simulating root distribution of plant individual with a three-dimensional model

AimsSoil resources distribute heterogeneously in natural ecosystems, which is known as soil heterogeneity. Responses of plant roots to soil heterogeneity are important for their adaptation to the changes of environments. However, few observations of root systems in soil are recorded, and there is a lack of comprehensive theory for roots responses to soil heterogeneity. MethodsSoil heterogeneity including configurational heterogeneity and qualitative heterogeneity is developed in three dimensions via extending a stochastic spatially explicit lattice-based habitat model in two dimensions. Root dynamics of plants growing in such soil heterogeneity in three dimensions are simulated. In these simulations, three parameters (i.e. ratio of root distributed in resource-rich patches, mean root length and root number of plants) are measured. ResultsComparing with the previous two dimensional model of heterogeneous pattern formation, this three-dimensional model is time-saving and more efficiency. The spatial pattern of habitats, as well as the adaptability of plants to soil heterogeneity, determines how plants place their roots into resource-rich and resource-poor patches. The plasticity of root architecture plays an important role in the root response to soil heterogeneity. Specifically, plants with the ability to respond to resources would adopt a large number of roots with short root length as the ratio of resource-rich patches is relatively small, while they would adopt a small number of roots with long root length as the ratio of resource-rich patches is relatively large. ConclusionsSuch realistic three-dimensional model allows us to better explore the effects of soil heterogeneity on plant root dynamics. Applying this model in root dynamics improves our understanding of root distribution in spatial habitat and of the formation mechanism of root morphology.

Trophic Niche and Diversity of a Pelagic Squid (Dosidicus gigas): A Comparative Study Using Stable Isotope, Fatty Acid, and Feeding Apparatuses Morphology

Commercially important pelagic squid such as jumbo squid Dosidicus gigas play a key role in energy and nutrient transfer in marine ecosystem. Geographic variations in the foraging strategy of D. gigas can provide valuable information on cephalopod trophic diversification and adaption to natural and anthropogenic impacts. Here, we used a novel combination of stable isotope (SI) and fatty acid (FA) analyses of muscle tissues and morphometric measurements of feeding apparatuses to evaluate spatial patterns of habitat and resource use of D. gigas collected from three main fishing grounds in the tropical and southern temperate Pacific Ocean. Results showed that the δ13C and δ15N values were different among geographic stocks within D. gigas, possibly because of site-specific dietary sources and isotopic baseline values. Spatial variabilities in FA profiles/biomarkers, combined with SI results, suggests two foraging patterns: utilization of resources mainly from oceanic food webs or from both neritic and oceanic food webs. This behavior likely promotes the site-specific trophic niche width, increases adaptation to inhabiting oceanographic conditions, and could be driven by differences in feeding apparatuses morphology, body size, and trophic position. These results emphasize the dietary flexibility of D. gigas and their important ecological roles in ecosystems as cross-biome trophic linkages. Trophic diversification of pelagic squid like D. gigas may have important implications for their population dynamics, ecological associations, and subsequent management.

Deep learning for coastal resource conservation: automating detection of shellfish reefs

AbstractIt is increasingly important to understand the extent and health of coastal natural resources in the face of anthropogenic and climate‐driven changes. Coastal ecosystems are difficult to efficiently monitor due to the inability of existing remotely sensed data to capture complex spatial habitat patterns. To help managers and researchers avoid inefficient traditional mapping efforts, we developed a deep learning tool (OysterNet) that uses unoccupied aircraft systems (UAS) imagery to automatically detect and delineate oyster reefs, an ecosystem that has proven problematic to monitor remotely. OysterNet is a convolutional neural network (CNN) that assesses intertidal oyster reef extent, yielding a difference in total area between manual and automated delineations of just 8%, attributable in part to OysterNet's ability to detect oysters overlooked during manual demarcation. Further training of OysterNet could enable assessments of oyster reef heights and densities, and incorporation of more coastal habitat types. Future iterations will be applied to high‐resolution satellite data for effective management at larger scales.

Assessing drivers of rocky reef fish biomass density from the Southern California Bight

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 628:125-140 (2019) - DOI: https://doi.org/10.3354/meps13103 Assessing drivers of rocky reef fish biomass density from the Southern California Bight Daniel J. Pondella II1,*, Susan E. Piacenza1,2, Jeremy T. Claisse1,3, Chelsea M. Williams1,3, Jonathan P. Williams1, Amanda J. Zellmer1, Jennifer E. Caselle4 1Vantuna Research Group, Occidental College, Los Angeles, CA 90041, USA 2Department of Biology, University of West Florida, Pensacola, FL 32514, USA 3Department of Biological Sciences, California State Polytechnic University, Pomona, CA 91768, USA 4Marine Science Institute, University of California, Santa Barbara, CA 93106, USA *Corresponding author: pondella@oxy.edu ABSTRACT: Biomass is often used as a metric for valuing and comparing ecosystems. However, many variables contribute to the amount of biomass an ecosystem can maintain; thus, it is necessary to evaluate the driving forces of patterns in biomass variation across sites. Here we utilized datasets associated with nearshore rocky reef fish biomass that span the Southern California Bight, using generalized least-squares modelling within the information-theoretic approach. Fish density, length, invertebrate density, algal density, and reef characteristics (relief and substrate type) were visually sampled at 89 sites, via SCUBA, with a random stratified sampling design using belt transects across depth strata in <20 m depth. Fish data were converted to biomass using published length-weight relationships. In addition to our visual surveys, spatially resolved explanatory variables included sea surface temperature, sea surface chlorophyll a, maximum wave height, kelp biomass, fishery harvest intensity index, a new remote sensing method for calculating reef slope, and distance to the 200 m isobath, a novel characteristic. All models in the confidence model set (ΔAICc <2) for total fish biomass included the variables sea surface temperature, distance to the 200 m isobath, sea surface chl a, slope, and the standard deviation of substrate and relief. Not all rocky reefs in the SCB equally support high densities of fish biomass, and our results suggest that an optimal combination of physical characteristics and forcing drive this variation. The novel variable, distance to the 200 m isobath, may be applicable for understanding fish biomass variation in marine ecosystems. KEY WORDS: Spatial habitat patterns · Reef fish biomass density · Generalized least-squares modeling · Remote sensing · Southern California Bight Full text in pdf format Supplementary material PreviousNextCite this article as: Pondella DJ II, Piacenza SE, Claisse JT, Williams CM, Williams JP, Zellmer AJ, Caselle JE (2019) Assessing drivers of rocky reef fish biomass density from the Southern California Bight. Mar Ecol Prog Ser 628:125-140. https://doi.org/10.3354/meps13103 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 628. Online publication date: October 10, 2019 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2019 Inter-Research.

The Role of Modeling in Monarch Butterfly Research and Conservation

Models are an integral part of the scientific endeavor, whether they be conceptual, mathematical, statistical, or simulation models. Models of appropriate complexity facilitate comprehension and improve understanding of the variables driving system processes. In the context of conservation planning decision-making or research efforts, a useful model reduces statistical noise by including only essential elements. Models can serve two related, but different purposes: understanding and prediction of future system behavior. Predictive models can require several iterations of refinement and empirical data gathering to be useful for conservation planning. Models with less predictive ability can be used to enhance understanding of system function and generate hypotheses for empirical evaluation. Modeling monarch butterfly systems, whether it be landscape-scale movement in breeding habitats, migratory behavior, or population dynamics at monthly or yearly timeframes, is challenging because the systems encompass complex spatial and temporal interactions across nested scales that are difficult, if not impossible, to empirically observe or comprehend without simplification. We review mathematical, statistical, and simulation models that have provided insights into monarch butterfly systems. Mathematical models have provided understanding of underlying processes that may be driving monarch systems. Statistical models have provided understanding of patterns in empirical data, which may represent underlying mechanisms. Simulations models have provided understanding of mechanisms driving systems and provide the potential to link mechanisms with data to build more predictive models. As an example, recently published agent-based models of non-migratory eastern North American monarch butterfly movement and egg-laying may provide the means to explore how different spatial patterns of habitat, habitat quality, and the interaction of stressors can influence future adult recruitment. The migratory process, however, has not been addressed with agent-based modeling. Using western monarch migration as an example, we describe how modeling could be used to provide insights into migratory dynamics. Future integration of migratory models with non-migratory and population dynamics models may provide better understanding and ultimately prediction of monarch butterfly movement and population dynamics at a continental scale.

Landscape heterogeneity in landform and land use provides functional resistance to gene flow in continuous Asian black bear populations.

ContextGenetic diversity is one of the most important facets of biological diversity, and changes in the spatial pattern of habitats, often modified by human activity, are believed to have affected the genetic diversity of resident natural populations.ObjectivesWe undertook a landscape genetic analysis in order to determine which landscape features influence gene flow within Asian black bear populations and to identify the underlying processes.MethodsIn our evaluation of gene flow, we estimated four parameters of resistance with regard to landscape elevation: the mean, the difference between the highest and lowest, the standard deviation, and the coefficient of variation of elevation among individuals. We then examined the resistance effect of different land use types.ResultsWith the exception of mean elevation, we found that all parameters showed a significant relationship with genetic distance, indicating that unevenness in elevation provides functional resistance to gene flow. Although we found no evidence of landscape barriers (isolation‐by‐barrier), there was an indication of landscape resistance (isolation‐by‐resistance). Urban area and farmland are suggested to be the strong factors contributing to the resistance to gene flow, even though isolation‐by‐distance was also detected. When we examined gene flow for pairs of males and pairs of females, both isolation‐by‐distance and isolation‐by‐resistance were stronger in order of female pairs, male pairs, all individual pairs.ConclusionsWe conclude that landscape resistance was detectable with a high contrast in landscape heterogeneity and they are more influential on females than males.OPEN PRACTICES This article has been awarded Open Data badge. All materials and data are publicly accessible via the Open Science Framework at https://doi.org/10.5061/dryad.gn0qf16. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

Shifting habitat mosaic: identification and mapping

The Shifting Habitat Mosaic refers to temporal changes in the spatial pattern of habitats, through the flood and dry pulses. This work mapped the dynamics of a Shifting Mosaic associated with the flood and dry pulses in the Environmental Protection Area of the Banhado Grande (EPABG). The research was divided into four stages: i) acquisition of satellite images; ii) obtaining rainfall river basin data from the Gravataí River; iii) identification of the flood and dry pulses; and iv) mapping of the Shifting Mosaic in the wetlands of the EPABG. In large swaths of flooding, a shifting connectivity is established between the AUs of EPABG, providing several interactions among these wetlands, such as nutrient exchange, sediments and living organisms. It was found that in periods where the accumulation of precipitation in the last 30 days is greater than 200 mm, we have the presence of predominantly aquatic mosaic with the presence of predominantly aquatic animals and vegetation; In periods where the cumulative precipitation in the 30 days is less than 200 mm, a terrestrial mosaic predominates, with increase of ciliary vegetation.

The landscape structure change of the tropical lowland forest and its possible effect on tree species diversity in South Sumatra, Indonesia

Zulfikhar, Zulkifli H, Kadir S, Iskandar I. 2017. The landscape structure change of the tropical lowland forest and its possible effect on tree species diversity in South Sumatra, Indonesia. Biodiversitas 18: 916-927. The forest fragmentation is a spatial structure change, resulted in a reduction of the number and size of forest patches, and a subdivision and breaking up of the areas into smaller parcels. The key scientific challenges concern the significant gaps exist in our ability to understand the relationships between landscape structure, biodiversity and the output of ecosystem services at different spatial and temporal scales. The objective of this study was to examine the possible effect of changes in a spatial pattern of habitat against the changes of the tree species diversity. We apply a landscape structure analysis of the forest fragmentation, and then determine the principal component of the landscape metrics. We analyzed multitemporal satellite imageries from 1989 to 2013, that have been clipped as a sampling area. Thirty-two circular sampling areas with a radius of 2,500 meters were used. We conducted observations in 32 unit sample plots of biodiversity in the size of 20 meters by 50 meters, at a midpoint of the clip of area sampling. The results of a test of inter-correlation of all variable data showed that there were 15 of 23 landscape metrics of natural forest, reliable for the next analysis. We reduced the number of variables into three new variable groups using Principal Component Analysis (PCA). We produce three of six models of principal component multipleregression were significant (p &lt; 0.05) to predict the composition and diversity of tree species caused by a change of landscape metrics, namely the species richness index, species equity index, and basal area of a tree stand. The prediction models are beneficial in the planning of biodiversity conservation and restoration of the fragmented natural forest.

Expansion of cooperatively growing populations: Optimal migration rates and habitat network structures.

Range expansion of species is driven by the interactions among individual- and population-level processes and the spatial pattern of habitats. In this work we study how cooperatively growing populations spread on networks representing the skeleton of complex landscapes. By separating the slow and fast variables of the expansion process, we are able to give analytical predictions for the critical conditions that divide the dynamic behaviors into different phases (extinction, localized survival, and global expansion). We observe a resonance phenomenon in how the critical condition depends on the expansion rate, indicating the existence of an optimal strategy for global expansion. We derive the conditions for such optimal migration in locally treelike graphs and numerically study other structured networks. Our results highlight the importance of both the underlying interaction pattern and migration rate of the expanding populations for range expansion. We also discuss potential applications of the results to biological control and conservation.

Multicriterion trade‐offs and synergies for spatial conservation planning

Summary Nature conservation policies need to deliver on multiple criteria, including genetic diversity, population viability and species richness as well as ecosystem services. The challenge of integrating these may be addressed by simulation modelling. We used four models (MetaConnect, SPOMSIM, a community model and InVEST) to assess a variety of spatial habitat patterns with two levels of total habitat cover and realised at two spatial scales, exploring which landscape structures performed best according to five different conservation criteria assessed for four functional types of organisms (approximately representing trees, butterflies, small mammals and birds). The results display both synergies and trade‐offs: population size and pollination services generally benefitted more from fragmentation than did genetic heterozygosity, and species richness more than allelic richness, although the latter two varied considerably among the functional types. No single landscape performed best across all conservation criteria, but averaging over criteria and functional types, overall performance improved with greater levels of habitat cover and intermediate fragmentation (or less fragmentation in cases with lower habitat cover). Policy implications. Using four simulation models, we show that different conservation objectives must be traded off in spatial conservation planning, and that considering only a single taxon or criterion may result in suboptimal choices when planning reserve networks. Nevertheless, heterogeneous spatial patterns of habitat can provide reasonable compromises for multiple criteria.

Exploring future agricultural development and biodiversity in Uganda, Rwanda and Burundi: a spatially explicit scenario-based assessment

Competition for land is increasing as a consequence of the growing demands for food and other commodities and the need to conserve biodiversity and ecosystem services. Land conversion and the intensification of current agricultural systems continues to lead to a loss of biodiversity and trade-offs among ecosystem functions. Decision-makers need to understand these trade-offs in order to better balance different demands on land and resources. There is an urgent need for spatially explicit information and analyses on the effects of different trajectories of human-induced landscape change in biodiversity and ecosystem services. We assess the potential implications of a set of plausible socio-economic and climate scenarios for agricultural production and demand and model-associated land use and land cover changes between 2005 and 2050 to assess potential impacts on biodiversity in Uganda, Rwanda and Burundi. We show that different future socio-economic scenarios are consistent in their projections of areas of high agricultural development leading to similar spatial patterns of habitat and biodiversity loss. Yet, we also show that without protected areas, biodiversity losses are higher and that expanding protected areas to include other important biodiversity areas can help reduce biodiversity losses in all three countries. These results highlight the need for effective protection and the potential benefits of expanding the protected area network while meeting agricultural production needs.

Profit efficiency and habitat biodiversity: The case of upland livestock farmers in Ireland

This study evaluates the private economic value of biodiversity using original farm habitat data and farm household survey data from livestock farms in the hills and uplands of Ireland. Profit efficiency and biodiversity efficiency are estimated using a stochastic translog production frontier model. Results indicate that biodiversity plays a beneficial role in farm profitability but the effect is non-linear. Stocking rate and the perimeter to area ratio are found to have a negative effect on biodiversity while the number of plots per farm has a positive effect. Biodiversity efficiency is influenced by the number of livestock, habitat quality, spatial habitat patterns and arrangement, perimeter to area ratio, the number of plots, profit efficiency and farmer's education and age.

Riverine Landscape Patch Heterogeneity Drives Riparian Ant Assemblages in the Scioto River Basin, USA.

Although the principles of landscape ecology are increasingly extended to include riverine landscapes, explicit applications are few. We investigated associations between patch heterogeneity and riparian ant assemblages at 12 riverine landscapes of the Scioto River, Ohio, USA, that represent urban/developed, agricultural, and mixed (primarily forested, but also wetland, grassland/fallow, and exurban) land-use settings. Using remotely-sensed and ground-collected data, we delineated riverine landscape patch types (crop, grass/herbaceous, gravel, lawn, mudflat, open water, shrub, swamp, and woody vegetation), computed patch metrics (area, density, edge, richness, and shape), and conducted coordinated sampling of surface-active Formicidae assemblages. Ant density and species richness was lower in agricultural riverine landscapes than at mixed or developed reaches (measured using S [total number of species], but not using Menhinick’s Index [D M]), whereas ant diversity (using the Berger-Park Index [DBP]) was highest in agricultural reaches. We found no differences in ant density, richness, or diversity among internal riverine landscape patches. However, certain characteristics of patches influenced ant communities. Patch shape and density were significant predictors of richness (S: R 2 = 0.72; D M: R 2=0.57). Patch area, edge, and shape emerged as important predictors of DBP (R 2 = 0.62) whereas patch area, edge, and density were strongly related to ant density (R 2 = 0.65). Non-metric multidimensional scaling and analysis of similarities distinguished ant assemblage composition in grass and swamp patches from crop, gravel, lawn, and shrub as well as ant assemblages in woody vegetation patches from crop, lawn, and gravel (stress = 0.18, R 2 = 0.64). These findings lend insight into the utility of landscape ecology to river science by providing evidence that spatial habitat patterns within riverine landscapes can influence assemblage characteristics of riparian arthropods.

Impacts of anthropogenic land-use change on populations of the Endangered Patagonian cypress Fitzroya cupressoides in southern Chile: implications for its conservation

AbstractLand-use change can have negative effects on threatened species by modifying their habitat and population dynamics. The habitat of the Endangered Patagonian cypress Fitzroya cupressoides (Mol.) Johnst. (Cupressaceae), a conifer endemic to the temperate forests of southern Chile and Argentina, has been transformed by land-use change and overexploitation. The impact of land-use change on the spatial pattern of F. cupressoides habitat from 1999 to 2011 was evaluated at the landscape level, using satellite images. Additionally, eight 20 × 25 m plots were established in four populations to assess their status. In each plot the density of F. cupressoides and the species richness of the associated plant communities were recorded and analysed, together with spatial patterns at the population and community levels. The loss of potential F. cupressoides habitat was 46% from 1999 to 2011 (38–100% for the four populations). The density of F. cupressoides was lowest in smaller habitat patches, where the number of plant species was higher and the matrix dominated by anthropogenic land-use. Land-use change was associated with a loss of potential habitat for F. cupressoides, and differences in the spatial patterns of habitat influenced the composition of remaining populations and communities. A landscape approach is recommended as a conservation planning strategy for F. cupressoides.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23millionha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC=0.809±0.011), but the performance of the Landsat-based map was within acceptable limits (AUC=0.717±0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat=9962ha, nesting habitat only=6036ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Development of an invasive species distribution model with fine-resolution remote sensing

Saltcedar (Tamarix spp.) is recognized as one of the most aggressively invasive species throughout the Western United States. Mapping its suitable habitat is of paramount importance to effective management, and thus, becomes a high priority for conservation practitioners. In previous studies, species distribution models (SDMs) have been applied to predicting the suitable habitats of saltcedar at national scale, but at coarser spatial resolution (1km). Although such studies achieved some success, they are lacking of capability to accommodate fine-scale resolution environmental variables, and therefore, fail to uncover detailed spatial pattern of habitats. The objective of this study was to develop a remote sensing driven SDM so as to characterize suitable habitats of saltcedar at very fine spatial scale (30m). We exploited several fine-scale environmental predictors through remote sensing images, and utilized the logistic regression model to analyze the species–habitat relationship by identifying influential factors with subset selection criteria. We also incorporated the spatial autocorrelation with regression kriging method. Our results indicated that the model incorporating spatial autocorrelation achieved a higher accuracy than that of regression only model. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the harmonic analysis were regarded as the most significant in predicting the invasive potential of saltcedar. We conclude that remote sensing driven SDM has potential to identify the suitable habitat of saltcedar at a fine scale and locate appropriate areas at high risk of saltcedar infestation, which could benefit the early control and proactive management strategies to a large extent.