Detecting biodiversity refugia using remotely sensed data

Context

Habitats characterized by improved soil moisture availability can function as microrefugia (hereafter referred to as “refugia”) for the persistence of rare plant species in dry environments. Such areas are dominated by Mediterranean woody vegetation (shrubland and woodland). An analysis of these refugia elucidates their spatial distribution at the landscape scale.

Objectives

Explore whether potential refugia, detected using the upper quantile of the normalized difference vegetation index (NDVI), are related, in space and time, with the survivability of rare species in dry environments.

Methods

We used upper NDVI quantile (25%) values to predict potential refugia in nine selected areas in northern parts of Israel from 1992 to 2011. Next, we developed an index based on the ratio of density (number of observations per area) of rare species in non-refugia versus refugia patches, per site (density of rare species index, DRSI). Finally, we examined the temporal stability of the DRSI using ANOVA and Augmented Dickey–Fuller (ADF) tests.

Results

Refugia classifications and DRSI values for all areas were stable over time (1992–2011). The DRSI values were significantly lower than 1; that is, the density of rare species in the predicted refugia areas was higher than in non-refugia areas.

Conclusions

We assumed that patches of dense woody vegetation, determined by the upper 25% quantile of the NDVI, could be used to identify potential biodiversity refugia in dry environments. This assumption was validated by the DRSI results; it confirms that the local conditions in refugia support rare species.

     

Rangeland biodiversity assessment using fine scale on-ground survey,time series of remotely sensed ground cover and climate data: an Australian savanna case study

Savanna rangelands are undergoing rapid environmental change and the need to monitor and manage landscape health is becoming increasingly an imperative of government agencies and research organizations. Remotely sensed ecological indicators of disturbance offer a potential approach, particularly in the context of issues of scale required to assess and monitor extensive rangeland areas. The objective of this research is to analyse the potential of spatially explicit ecological indicators of disturbance to explain the spatial variability in species diversity and abundance (including introduced flora species) in rangelands. For two mapped rangeland ecosystem types in northern Australia, regression analysis was used to explore the relationships between species diversity and abundance, and remotely sensed ground cover time series statistics, foliage projective cover, and a precipitation deficit index. It was assumed that the ecosystem types used had been mapped to represent uniform vegetation units and consequently predictors of environmental heterogeneity were not used in the regression analysis. It was found that the predictor variables performed well in explaining the variation in species diversity and abundance for the more open, homogenous and less topographically complex basalt ecosystem type and less effectively for the more structurally complex, more wooded and less disturbed metamorphic ecosystem type. The results indicate that, for mapped ecosystem types with low heterogeneity and topographic complexity, ground cover temporal mean and variance are potentially useful indicators of disturbance to species diversity and abundance, provided the local spatial variability in the climate signal is accounted for.     

The feasibility of remotely sensed data to estimate urban tree dimensions and biomass

Accurately measuring the biophysical dimensions of urban trees, such as crown diameter, stem diameter, height, and biomass, is essential for quantifying their collective benefits as an urban forest. However, the cost of directly measuring thousands or millions of individual trees through field surveys can be prohibitive. Supplementing field surveys with remotely sensed data can reduce costs if measurements derived from remotely sensed data are accurate. This study identifies and measures the errors incurred in estimating key tree dimensions from two types of remotely sensed data: high-resolution aerial imagery and LiDAR (Light Detection and Ranging). Using Sacramento, CA, as the study site, we obtained field-measured dimensions of 20 predominant species of street trees, including 30–60 randomly selected trees of each species. For each of the 802 trees crown diameter was estimated from the aerial photo and compared with the field-measured crown diameter. Three curve-fitting equations were tested using field measurements to derive diameter at breast height (DBH) (r² = 0.883, RMSE = 10.32 cm) from the crown diameter. The accuracy of tree height extracted from the LiDAR-based surface model was compared with the field-measured height (RMSE = 1.64 m). We found that the DBH and tree height extracted from the remotely sensed data were lower than their respective field-measured values without adjustment. The magnitude of differences in these measures tended to be larger for smaller-stature trees than for larger stature species. Using DBH and tree height calculated from remotely sensed data, aboveground biomass (r² = 0.881, RMSE = 799.2 kg) was calculated for individual tree and compared with results from field-measured DBH and height. We present guidelines for identifying potential errors in each step of data processing. These findings inform the development of procedures for monitoring tree growth with remote sensing and for calculating single tree level carbon storage using DBH from crown diameter and tree height in the urban forest.     

Stability of ecosystem functioning and diversity of grasslands at the landscape scale

Diversity often increases ecosystem functioning and enhances stability, but this relationship has been evaluated at the community scale and considering, for the most part, only species richness. Here, we explored the relationship between landscape diversity and either the coefficient of variation or the interannual standard deviation of greenness in Pampean grasslands and Patagonian meadows, and tried to elucidate the mechanisms responsible for the resulting patterns. The coefficient of variation decreased with increasing landscape richness in Pampas but remained constant in Patagonia, while the interannual standard deviation of greenness decreased in both regions. The diversity–variability relationship in Pampean grasslands was largely accounted for by the mechanism of statistical averaging, while in Patagonian meadows, it was accounted for by a combination of statistical averaging, mean–variance rescaling and positive covariation of landscape units. There were no cases of negative covariance among landscape units. This is the first demonstration that landscape diversity increases stability of ecosystem functioning.     

Functional and structural landscape indicators of intensification,resilience and resistance in agroecosystems in southern Argentina based on remotely sensed data

There is increasing interest in developing criteria to evaluate the environmental implications of intensive agricultural land use. This implies discriminating between nature and man-made effects upon structural and functional attributes of agroecosystems. Adequate indicators of these combined effects should be cost efficient yet compatible with the core of ecological theory on biodiversity, spatial organization and ecosystem stability. We developed resistance-resilience metrics of plant growth to evaluate the intensity of agricultural use in a temperate irrigated basin in southern Argentina. The metrics are based on an analysis of the components of a temporal series of vegetation indices computed at a low resolution from available globally remote sensed reflectance imagery. We related the developed metrics to the properties of the soils and plant canopies observed at field scale and high-resolution imagery of the basin. Soil depth, soil erosion status and land fragmentation account for large fractions of the variance of the distribution of functional groups of the plant canopies and are also correlated with smaller scale attributes of land vegetation cover. Resistance-resilience indicators constitute a cost-efficient and adequate approach to evaluate the degree of intensification of land agricultural use.     

Science for action at the local landscape scale

For landscape ecology to produce knowledge relevant to society, it must include considerations of human culture and behavior, extending beyond the natural sciences to synthesize with many other disciplines. Furthermore, it needs to be able to support landscape change processes which increasingly take the shape of deliberative and collaborative decision making by local stakeholder groups. Landscape ecology as described by Wu (Landscape Ecol 28:1–11, 2013) therefore needs three additional topics of investigation: (1) the local landscape as a boundary object that builds communication among disciplines and between science and local communities, (2) iterative and collaborative methods for generating transdisciplinary approaches to sustainable change, and (3) the effect of scientific knowledge and tools on local landscape policy and landscape change. Collectively, these topics could empower landscape ecology to be a science for action at the local scale.     

Modelling tree canopy cover and evaluating the driving factors based on remotely sensed data and machine learning

Quantifying urban tree cover is important to ensure sustainable urban ecosystem. This study calculates urban percent tree cover (PTC) for Bursa city, Turkey from Sentinel-2 data and evaluates the driving factors of PTC using an Artificial Neural Network-Multi Layer Perception (ANN-MLP) approach. For the PTC calculation, a Regression Tree (RT) analysis was performed using several vegetation indices (NDVI, LAI, fCOVER, MSAVI2, and MCARI) to improve accuracy. Socio-economic, topographic, and biophysical variables were incorporated into the ANN-MLP approach to evaluate the factors that drive urban PTC. A PTC prediction map was generated with an accuracy of 0.95 and a coefficient of determination of 0.87. The ANN-MLP training process yielded a correlation coefficient value of 0.71 and an R-square of 0.82 was achieved between the predicted ANN-MLP and observed tree cover maps. A priority tree cover map was generated considering statistical relationships between the factors and the ANN-MLP prediction map in addition to visual interpretations at the urban scale. Results demonstrate that, unlike other urban forms, PTC has a statistically negative relationship with the gross dwelling density (R2 =0.31). Topographic variables including slope and DEM were positively correlated with PTC with the R2 value of 0.80 and 0.72 respectively. The integration of remote sensing data with vegetation indices and driving factors yielded accurate prediction for identifying and evaluating the variability in the urban PTC.     

A Bayesian test of hierarchy theory: scaling up variability in plant cover from field to remotely sensed data

Hierarchy theory predicts that a hierarchy of process rates should be reflected in a hierarchy of spatial and temporal scales observable on the landscape. We will show that multiple scales of pattern for total plant cover measured in the field at 1-m resolution are correlated with scales of vegetative pattern obtained from remotely sensed data with resolutions of 25 m² and 30 ². Second, using four models based on postulates of hierarchy theory, we will combine the scales of pattern of each individual species within a community to estimate the remotely sensed community scales of pattern. Finally, we will compare the four models using a Bayesian analysis to determine which model best portrays how vegetative patterns of individual species combine to produce remotely observed community patterns. The results of the model comparisons provide an example of how postulates of hierarchy theory can be tested and how individual species patterns can be scaled-up to estimate remotely observed scales of pattern.     

Lightning,precipitation and vegetation at landscape scale

Landscape Ecology - We investigated the question “Is there a relationship between seasonality in precipitation and vegetative cover in Pole Canyon, NM?” GIS and statistical methods were...     

Combining process-based models for future biomass assessment at landscape scale

We need an integrated assessment of the bioenergy production at landscape scale for at least three main reasons: (1) it is predictable that we will soon have landscapes dedicated to bioenergy productions; (2) a number of “win–win” solutions combining several dedicated energy crops have been suggested for a better use of local climate, soil mosaic and production systems and (3) “well-to-wheels” analyses for the entire bioenergy production chain urge us to optimize the life cycle of bioenergies at large scales. In this context, we argue that the new generation of landscape models allows in silico experiments to estimate bioenergy distributions (in space and time) that are helpful for this integrated assessment of the bioenergy production. The main objective of this paper was to develop a detailed modeling methodology for this purpose. We aimed at illustrating and discussing the use of mechanistic models and their possible association to simulate future distributions of fuel biomass. We applied two separated landscape models dedicated to human-driven agricultural and climate-driven forested neighboring patches. These models were combined in the same theoretical (i.e. virtual) landscape for present as well as future scenarios by associating realistic agricultural production scenarios and B2-IPCC climate scenarios depending on the bioenergy type (crop or forest) concerned in each landscape patch. We then estimated esthetical impacts of our simulations by using 3D visualizations and a quantitative “depth” index to rank them. Results first showed that the transport cost at landscape scale was not correlated to the total biomass production, mainly due to landscape configuration constraints. Secondly, averaged index values of the four simulations were conditioned by agricultural practices, while temporal trends were conditioned by gradual climate changes. Thirdly, the most realistic simulated landscape combining intensive agricultural practices and climate change with atmospheric CO₂ concentration increase corresponded to the lowest and unwanted bioenergy conversion inefficiency (the biomass production ratio over 100 years divided by the averaged transport cost) and to the most open landscape. Managing land use and land cover changes at landscape scale is probably one of the most powerful ways to mitigate negative (or magnify positive) effects of climate and human decisions on overall biomass productions.     

Sensitivity of ecosystem goods and services projections of a forest landscape model to initialization data

Projections of indicators of forest ecosystem goods and services (EGS) based on process-based landscape models are critical for adapting forest management to climate change. However, the scarcity of fine-grained, spatially explicit forest data means that initializing these models is both a challenge and a source of uncertainty. To test how different initialization approaches influence the simulation of forest dynamics and EGS indicators we initialized the forest landscape model LandClim with fine resolution empirical data, coarse empirical data, and simulation-derived data, and evaluated the results at three spatial scales (stand, management area and landscape). Simulations were performed for a spruce (Picea abies) dominated landscape in the Black Forest, Germany, under current climate and a climate change scenario. We found that long-term (>150 years) projections are robust to initialization uncertainty. In contrast, shorter-term projections are sensitive to initialization uncertainty, with sensitivity increasing when EGS are assessed at smaller spatial scales, and when the EGS indicators depend on the spatial distribution of individual species. EGS dynamics are strongly influenced by interactions between the density, species composition, and age structure of initialized forests and simulated forest management. If EGS dynamics are strongly influenced by climate change, such as when climate change induces mortality in drought-sensitive species, some of the initialization uncertainty can be masked. We advocate for initializing landscape models with fine-grained data in applications that focus on spatial management problems in heterogeneous landscapes, and stress that the scale of analysis must be in accordance with the accuracy that is warranted by the initialization data.     

A general model to quantify ecological integrity for landscape assessments and US application

Increasingly, natural resources agencies and organizations are using measures of ecological integrity to monitor and evaluate the status and condition of their landscapes, and numerous methods have been developed to map the pattern of human activities. In this paper I apply formal methods from decision theory to develop a transparent ecological indicator of landscape integrity. I developed a parsimonious set of stressors using an existing framework to minimize redundancy and overlap, mapping each variable as an individual data layer with values from 0 to 1.0, and then combined them using an “increasive” function called fuzzy sum. A novel detailed land use dataset is used to generate empirical measures of the degree of human modification to map important stressors such as land use, land cover, and presence, use, and distance from roads. I applied this general framework to the US and found that the overall average degree of human modification was 0.375. Regional variation was fairly predictable, but aggregation of these raw values into terrestrial or watershed units resulted in large differences at local to regional scales. I discuss three uses of these data by land managers to manage protected areas within a dynamic landscape context. This approach generates an internally-valid model that has a direct, empirical, and physical basis to estimate the degree of human modification.     

Relating tradable credits for biodiversity to sustainability criteria in a dynamic landscape

Tradable biodiversity credit systems provide flexible means to resolve conflicts between development and conservation land-use options for habitats occupied by threatened or endangered species. We describe an approach to incorporate the influence of habitat fragmentation into the conservation value of tradable credits. Habitat fragmentation decreases gene flow, increases rates of genetic drift and inbreeding, and increases probabilities of patch extinction. Importantly, tradable credit systems will change the level of fragmentation over time for small and/or declining populations. We apply landscape equivalency analysis (LEA), a generalizable, landscape-scale accounting system that assigns conservation value to habitat patches based on patch contributions to abundance and genetic variance at landscape scales. By evaluating habitat trades using two models that vary the relationship between dispersal behaviors and landscape patterns, we show that LEA provides a novel method for limiting access to habitat at the landscape-scale, recognizing that the appropriate amount of migration needed to supplement patch recruitment and to offset drift and inbreeding will vary as landscape pattern changes over time. We also found that decisions based on probabilities of persistence alone would ignore changes in migration, genetic drift, and patch extinction that result from habitat trades. The general principle of LEA is that habitat patches traded should make at least equivalent contributions to rates of recruitment and migration estimated at a landscape scale. Traditional approaches for assessing the “take” and “jeopardy” standards under the Endangered Species Act based on changes in abundance and probability of persistence may be inadequate to prevent trades that increase fragmentation.     

Quantifying habitat specificity to assess the contribution of a patch to species richness at a landscape scale

Assessing and predicting the species richness of a complex landscape remains a problem because there is no simple scaling function of species richness in a heterogeneous environment. Furthermore, the potential value of an area for biodiversity conservation may depend on which, rather than how many, species the area contains. This paper shows how we can objectively evaluate the contribution of an area, e.g., a habitat patch, to larger-scale plant species richness, e.g., a landscape composed of patches of several habitat types, and how we can test hypotheses that attempt to explain this contribution. We quantified the concept of habitat specificity to assess the proportion of each observed plant population that is concentrated within a given spatial element. A case study of a biodiversity-monitoring program in the Swiss Canton of Aargau showed that the relative contribution of the three main types of land use to the overall species richness differed strongly between higher taxa (vascular plants and molluscs). However, the type of data, i.e., presence-absence or abundance, was not important. Resampling of the plant data suggested that stratification provided an unbiased estimate of relative specificity, whereas unstratified sampling caused bias even for large samples. In a second case study of vascular plants in an agricultural landscape in central Switzerland, we tested whether the type, size or shape of a landscape element can predict its contribution to the species richness of the landscape. Habitat types that were less frequently disturbed contributed more per m² to landscape species richness than more frequently disturbed ones. Contrary to expectation, patch size was negatively correlated to specificity per m² for arable fields, whereas patch shape appeared to be unrelated to the specificity per m² both for arable fields and for meadows. The specificity approach provides a solution to the problem of scaling species richness and is ideally suited for testing hypotheses on the effect of landscape structure on landscape species richness. Specificity scores can easily be combined with measures of other aspects of rarity to assess the contribution of a spatial element to conservation goals formulated at regional, national or global level.     

Integrating landscape resistance and multi-scale predictor of habitat selection for amphibian distribution modelling at large scale

Context

Species distribution modelling is a common tool in conservation biology but two main criticisms remain: (1) the use of simplistic variables that do not account for species movements and/or connectivity and (2) poor consideration of multi-scale processes driving species distributions.

Objectives

We aimed to determine if including multi-scale and fine-scale movement processes in SDM predictors would improve accuracy of SDM for low-mobility amphibian species compared with species-level analysis.

Methods

We tested and compared different SDMs for nine amphibian species with four different sets of predictors: (1) simple distance-based predictors; (2) single-scale compositional predictors; (3) multi-scale compositional predictors with a priori selection of scale based on knowledge of species mobility and scale-of-effect; and (4) multi-scale compositional predictors calculated using a friction-based functional grain to account for resource accessibility with landscape resistance to movement.

Results

Using friction-based functional grain predictors produced slight to moderate improvements of SDM performance at large scale. The multi-scale approach, with a priori scale selection, led to ambiguous results depending on the species studied, in particular for generalist species.

Conclusion

We underline the potential of using a friction-based functional grain to improve SDM predictions for species-level analysis.

     

Blowing litter across a landscape: effects on ecosystem nutrient flux and implications for landscape management

Lateral flows in landscape mosaics represent a fundamentally important process in landscape ecology, but are still poorly understood in general. For example, windblown litter nutrient transfer across a landscape has rarely been studied from an ecosystem perspective. In this study we measured the litter nutrient transfer from an Acacia mangium plantation to a Dimocarpus longan orchard in an agroforestry landscape for 3 years from January 2002 to December 2004. About 11% of the total litterfall of the acacia plantation were transported to the longan orchard annually, accounting for ca. 9–59% of the total litter nutrient input of the longan orchard. The windblown litter transfer showed high spatial variation mainly caused by wind speed and directions. Slope positions 5 m away from the source acacia plantation received significantly greater amount of allochthonous acacia litter than those 10 m away, and the northwest-facing slope of the longan orchard received 2 to 3-fold more litter than the southeast- and south-facing slopes because of the prevailing southeasterly wind in the region. To explore how different management practices may influence the litterfall, leaf production, and soil nutrient status of the two ecosystems, we developed a Meta-Ecosystem Litter Transfer (MELT) model to simulate the processes of litter-related transformation (production, deposition, and decomposition) and transfer (wind- and management-driven movement). Our simulation results suggest that less than 30% of acacia litter should be transferred to the longan orchard in order for the acacia plantation to sustain itself and maximize production of the longan. Connectivity of nutrient flow between adjacent ecosystems as shown here leads to a functional meta-ecosystem with higher landscape-scale production of ecosystem services. That is, managing this connectivity through landscape design or active litter transfers can lead to large changes in overall landscape functioning and service production.     

Multi-functional landscapes in semi arid environments: implications for biodiversity and ecosystem services

Synergies between biodiversity conservation objectives and ecosystem service management were investigated in the Succulent Karoo biome (83,000 km²) of South Africa, a recognised biodiversity hotspot. Our study complemented a previous biodiversity assessment with an ecosystem service assessment. Stakeholder engagement and expert consultation focussed our investigations on surface water, ground water, grazing and tourism as the key services in this region. The key ecosystem services and service hotspots were modelled and mapped. The congruence between these services, and between biodiversity priorities and ecosystem service priorities, were assessed and considered in relation to known threats. Generally low levels of overlap were found between these ecosystem services, with the exception of surface and ground water which had an 80% overlap. The overlap between ecosystem service hotspots and individual biodiversity priority areas was generally low. Four of the seven priority areas assessed have more than 20% of their areas classified as important for services. In specific cases, particular service levels could be used to justify the management of a specific biodiversity priority area for conservation. Adopting a biome scale hotspot approach to assessing service supply highlighted key management areas. However, it underplayed local level dependence on particular services, not effectively capturing the welfare implications associated with diminishing and limited service provision. We conclude that regional scale (biome level) approaches need to be combined with local level investigations (municipal level). Given the regional heterogeneity and varied nature of the impacts of drivers and threats, diverse approaches are required to steer land management towards sustainable multifunctional landscape strategies.     

Response of bioenergy landscape patterns and the provision of biodiversity ecosystem services associated with land-use changes in Jinghong County,Southwest China

Context

Dramatic land-use change has taken place in the tropical region of southwestern China. However, quantitative evaluation of changes in landscape sustainability and the provision of biodiversity ecosystem services (BESVs) of the region has seldom been attempted.

Objectives

This study was designed to: (1) assess bioenergy landscape dynamics based on graph theory; (2) predict bioenergy landscape sustainability in response to land-use changes, and (3) explore the effects of land-use changes on BESVs’ variation based on bioenergy modeling.

Methods

The PANDORA model, a bioenergy-based integrated evaluation of BESV related to landscape connectivity, was employed to analyze variations in landscape’s bioenergy and BESV in Jinghong County, southwestern China. In addition, we applied this model and several indices (change extent, change rate, and growth type) to evaluate responses of bioenergy and BESV to land-use changes.

Results

The bioenergy and bioenergy fluxes of the regional landscape have decreased since the 1970s, while the landscape has remained sustainable with a high level of bioenergy. The BESVs overall fluctuated from $8.41 m^?2 year^?1 in the 1970s to $8.54, 7.45, and 5.71 m^?2 year^?1 in 1990, 2000, and 2010, respectively. Further, both changes in the land-use area and patterns, including change extent, change rate, and change pattern, affected the variation in BESVs.

Conclusions

The PANDORA model can evaluate bioenergy dynamics, sustainability, and BESV variations on the landscape scale effectively. Further, the BESV is sensitive to changes in landscape composition and pattern, and thus, increasing natural vegetation and landscape connectivity could improve provisions to conserve the landscape’s biodiversity.

     

Semi-natural vegetation and its relationship to designated urban green space at the landscape scale in Leeds, UK

The species composition of semi-natural vegetation in urban areas is influenced by a diversity of factors operating at a variety of spatial scales. This study investigates relationships at the landscape scale between species numbers of semi-natural plant communities and variations in the nature of designated urban green space. Species’ records were obtained from a survey of tetrads (2 km × 2 km) across a contiguous central area of built-up landscape and nearby satellite settlements in the metropolitan borough of Leeds, northern England. Plant species were categorised into natives, archaeophytes, neophytes, casuals and conservation-designated species. The type and extent of designated urban green space within a tetrad was determined using GIS. There was more built-up and designated green space area in the central urban area than in the satellite settlements. However, this difference was not reflected statistically significantly in plant category species’ numbers. Numbers of native species correlated positively with areas of green space designated for relatively high nature conservation value. Neophytes and casuals correlated positively with semi-natural green space lacking rare native species or high native species richness but designated principally for local community accessibility. The value of such spaces and the importance of their appropriate management, not only for community benefits like individual physical health and mental well-being, but also for overall urban plant biodiversity, is highlighted.