Quantifying the agricultural landscape and assessing spatio-temporal patterns of precipitation and groundwater use

Quantitative agricultural landscape indices are useful to describe functional relationships among climatic conditions, groundwater dynamics, soil properties and agricultural land use for mathematical models. We applied methods of regression statistics, variance component estimation and a Geographical Information System (GIS) to construct indices describing crops and soils and to establish functional relationships among these variables. This paper describes the development of indices and the partitioning of the spatial and temporal variation in groundwater models using the data from Tulare County, California, which was selected as the study area. Indices of ground surface elevation, total crop water demand, soil water infiltration rate, and soil production index explain 91% of the variation in average spring groundwater level. After relating spatial patterns of groundwater use to indices of crop and soil properties, we found that mean groundwater use is positively related to total crop water demand and soil water infiltration rate while the variation in groundwater use was negatively correlated with the crop water demand and soil water infiltration rate and positively related to soil water holding capacity. The spatial variation in groundwater use was largely influenced by crops and soil types while the temporal variation was not. We also found that groundwater use increased exponentially with decreasing annual precipitation for most townships. Based on these associations, groundwater use in each township can be forecast from relative precipitation under current methods of agricultural production. Although groundwater table depth is strongly affected by topography, the statistically significant indices observed in the model clearly show that agricultural land use influences groundwater table depth. These simple relationships can be used by agronomists to make water management decisions and to design alternative cropping systems to sustain agricultural production during periods of surface water shortages.     

Measuring landscape connectivity: On the importance of within-patch connectivity

Context

Many connectivity metrics have been used to measure the connectivity of a landscape and to evaluate the effects of land-use changes and potential mitigation measures. However, there are still gaps in our understanding of how to accurately quantify landscape connectivity.

Objectives

A number of metrics only measure between-patch connectivity, i.e. the connectivity between different habitat patches, which can produce misleading results. This paper demonstrates that the inclusion of within-patch connectivity is important for accurate results.

Methods

The behavior of two metrics is compared: the Connectance Index (CONNECT), which measures only between-patch connectivity, and the effective mesh size (m_eff), which includes both within-patch and between-patch connectivity. The connectivity values of both metrics were calculated on a set of simulated landscapes. Twenty cities were then added to these landscapes to calculate the resulting changes in connectivity.

Results

We found that when using CONNECT counter-intuitive results occurred due to not including within-patch connectivity, such as scenarios where connectivity increased with increasing habitat loss and fragmentation. These counter-intuitive results were resolved when using m_eff. For example, landscapes with low habitat amount may be particularly sensitive to urban development, but this is not reflected by CONNECT.

Conclusions

Applying misleading results from metrics like CONNECT can have detrimental effects on natural ecosystems, because reductions in within-patch connectivity by human activities are neglected. Therefore, this paper provides evidence for the crucial need to consider the balance between within-patch connectivity and between-patch connectivity when calculating the connectivity of landscapes.

     

Dispersal traits determine plant response to habitat connectivity in an urban landscape

Identification of trait syndromes that make species vulnerable to habitat fragmentation is essential in predicting biodiversity change. Plants are considered particularly vulnerable if their capacities for persistence in and for dispersal among local habitats are low. Here we investigated the influence of easily measured functional traits on the presence of 45 plant species in an urban landscape in north-west Germany where patches were separated by distances consistent with regular plant dispersal range. To describe the spatial configuration of patches we calculated species-specific patch connectivities. Then we assessed plant connectivity responses in distribution models calculated from connectivities and environmental predictors. Twenty (45%) of the analysed species showed a positive connectivity response after accounting for species-specific habitat requirements. These species differed from non-responsive species by functional traits associated with dispersal, including reduced seed numbers and higher terminal velocities relative to non-responsive species. Persistence traits played however no role which we attribute to the environmental conditions of urban habitats and their spatiotemporal characteristics. Our study underlines that even ruderal plants experience dispersal limitation and demonstrates that easily measured functional traits may be used as indicators of fragmentation vulnerability in urban systems allowing generalizations to larger species sets.     

Effect of landscape connectivity on plant communities: a review of response patterns

Landscape Ecology - Fragmentation in agricultural landscapes is considered as a major threat to biodiversity. Thus, ecological corridors are deployed at multiple scales to increase connectivity....     

Mapping landscape connectivity for large spatial extents

Context

Mapping landscape connectivity across large spatial extents is an important component of ecological reserve network designs and species recovery plans. It can, however, be limited by computational power. One way to overcome this problem is to split the study area into smaller tiles, map landscape connectivity within each of those tiles, and then merge tiles back together to form composite connectivity maps.

Objectives

We tested the effects of landscape structure on the accuracy of composite landscape connectivity maps created from tiles and tested two methods to increase this accuracy.

Methods

We correlated replicate, composite current density maps with untiled maps. We tested whether our findings depended on the composition of the landscape by testing maps with corridors, barriers, different mixtures of high- and low-cost habitat, and road networks.

Results

We found that composite current density maps underestimated large-scale connectivity and overestimated the contribution of small habitat patches to overall connectivity. These biases became more pronounced as the tiles became relatively smaller. Landscapes with corridors or barriers were particularly sensitive. We increased the accuracy of tiled maps by increasing pixel size or by averaging several maps created using a “moving window” approach.

Conclusions

There is a trade-off between tile size and pixel size when modelling connectivity across large spatial extents. We suggest using the largest tile size possible when tiling is necessary, in conjunction with increased pixel size and a moving window method to increase accuracy of the composite current density maps.

     

Genetic analysis of landscape connectivity in tree populations

Genetic connectivity in plant populations is determined by gene movement within and among populations. When populations become genetically isolated, they are at risk of loss of genetic diversity that is critical to the long-term survival of populations. Anthropogenic landscape change and habitat fragmentation have become so pervasive that they may threaten the genetic connectivity of many plant species. The theoretical consequences of such changes are generally understood, but it is not immediately apparent how concerned we should be for real organisms, distributed across real landscapes. Our goals here are to describe how one can study gene movement of both pollen and seeds in the context of changing landscapes and to explain what we have learned so far. In the first part, we will cover methods of describing pollen movement and then review evidence for the impact of fragmentation in terms of both the level of pollen flow into populations and the genetic diversity of the resulting progeny. In the second part, we will describe methods for contemporary seed movement, and describe findings about gene flow and genetic diversity resulting from seed movement. Evidence for pollen flow suggests high connectivity, but it appears that seed dispersal into fragments may create genetic bottlenecks due to limited seed sources. Future work should address the interaction of pollen and seed flow and attention needs to be paid to both gene flow and the diversity of the incoming gene pool. Moreover, if future work is to model the impact of changing landscapes on propagule movement, with all of its ensuing consequences for genetic connectivity and demographic processes, we will need an effective integration of population genetics and landscape ecology.     

Quantifying the effects of remnant seed sources on post-volcanic-eruption forest recovery through historic landscape reconstruction from 1710 to 2010

Landscape Ecology - The rate and trajectory of forest landscape recovery after a volcanic eruption rely largely on available seed sources and patterns of seed dispersal. However, quantifying the...     

Static vs dynamic connectivity: how landscape changes affect connectivity predictions in the Iberian Peninsula

Landscape Ecology - Climate and land-use changes affect species ranges and movements. However, these changes are usually overlooked in connectivity studies, and this could have adverse consequences...     

Quantifying and predicting population connectivity of an outbreaking forest insect pest

Landscape Ecology - Dispersal has a key role in the population dynamics of outbreaking species such as the spruce budworm (Choristoneura fumiferana) as it can synchronize the demography of distant...     

Quantifying the landscape influence on plant invasions in Mediterranean coastal habitats

Landscape pattern might be an important determinant of non-native plant invasions because it encompasses components influencing the availability of non-native plant propagules and disturbance regimes. We aimed at exploring the relative role of patch and landscape characteristics, compared to those of habitat type and regional human influence on non-native plant species richness. For this purpose, we identified all non-native plant species in 295 patches of four coastal habitat types across three administrative regions in NE Spain differing in the degree of human influence. For each patch, we calculated several variables reflecting habitat patch geometry (size and shape), landscape composition (distribution of land-cover categories) and landscape configuration (arrangement of patches). The last two groups of variables were calculated at five different spatial extents. Landscape composition was by far the most important group of variables associated with non-native species richness. Natural areas close to diverse and urban landscapes had a high number of non-native species while surrounding agricultural areas could buffer this effect. Regional human influence was also strongly associated with non-native species richness while habitat type was the least important factor. Differences in sensitivity of landscape variables across spatial extents proved relevant, with 100 m being the most influential extent for most variables. These results suggest that landscape characteristics should be considered for performing explicit spatial risk analyses of plant invasions. Consequently, the management of invaded habitats should focus not only at the stand scale but also at the highly influential neighbouring landscape. Prior to incorporate landscape characteristics into management decisions, sensitivity analyses should be taken into account to avoid inconsistent variables.     

A landscape connectivity approach to mitigating the urban heat island effect

Landscape Ecology - Urban integration has exacerbated the spreading of urban heat islands (UHIs) across cities. Blue/green landscapes embedded within urban areas, behaving as cool islands...     

Is landscape connectivity a dependent or independent variable?

With growing interest in landscape connectivity, it is timely to ask what research has been done and what re mains to be done. I surveyed papers investigating landscape connectivity from 1985 to 2000. From these papers, I determined if connectivity had been treated as an independent or dependent variable, what connectivity metrics were used, and if the study took an empirical or modeling approach to studying connectivity. Most studies treated connectivity as an independent variable, despite how little we know about how landscape structure and organism movement behaviour interact to determine landscape connectivity. Structural measures of connectivity were more common than functional measures, particularly if connectivity was treated as an independent variable. Though there was a good balance between modeling and empirical approaches overall – studies dealing with connectivity as a dependent, functional variable were mainly modeling studies. Based on the research achieved thus far, fu ture landscape connectivity research should focus on: (1) elucidating the relationship between landscape struc ture, organism movement behaviour, and landscape connectivity (e.g., treating connectivity as a dependent variable), (2) determining the relationships between different measures of connectivity, particularly structural and functional measures, and (3) empirically testing model predictions regarding landscape connectivity.This revised version was published online in May 2005 with corrections to the Cover Date.     

Considering landscape connectivity and gene flow in the Anthropocene using complementary landscape genetics and habitat modelling approaches

Landscape Ecology - A comprehensive understanding of how rapidly changing environments affect species gene flow is critical for mitigating future biodiversity losses. While recent methodological...     

Evaluation of landscape connectivity at community level using satellite-derived NDVI

Landscape connectivity, defined as the degree to which the landscape facilitates or impedes movement among resource patches, has been considered to be a key issue for biodiversity conservation. However, the use of landscape connectivity measurements has been strongly criticised due to uncertainties in the methods used and the lack of validation. Moreover, measurements are typically restricted to the population level, whereas management is generally carried out at the community level. Here, we used satellite imagery and network metrics to predict the landscape connectivity at community level for semi-natural herbaceous patches in an urban area near Paris (France). We tested different measurement methods, both taking into account and ignoring the spatial heterogeneity of matrix resistance estimated by the normalised difference vegetation index (NDVI), and quantifying the link strength between patches with the shortest path and flow metrics. We assessed the fit of these connectivity predictions with empirical data on plant communities embedded in an urban matrix. Our results indicate that the best fit with the empirical data is obtained when the connectivity is estimated with the flow metric and takes into account the matrix heterogeneity. Overall, our study helps to estimate the landscape connectivity of urban areas and makes recommendations for ways in which we might optimise landscape planning with respect to conservation of urban biodiversity.     

Sensitivity of resource selection and connectivity models to landscape definition

Context

The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention.

Objectives

We evaluated the sensitivity of resource selection and connectivity models to four landscape definition choices including (1) the type of geospatial layers used, (2) layer source, (3) thematic resolution, and (4) spatial grain.

Methods

We used GPS telemetry data from pumas (Puma concolor) in southern California to create multi-scale path selection function models (PathSFs) across landscapes with 2500 unique landscape definitions. To create the landscape definitions, we identified seven geospatial layers that have been shown to influence puma habitat use. We then varied the number, sources, spatial grain, and thematic resolutions of these layers to create our suite of plausible landscape definitions. We assessed how PathSF model performance (based on AIC) was affected by landscape definition and examined variability among the predicted probability of movement surfaces, connectivity models, and road crossing locations.

Results

We found model performance was extremely sensitive to landscape definition and identified only seven top models out of our suite of definitions (<1%). Spatial grain and the number of geospatial layers selected for a landscape definition significantly affected model performance measures, with finer grains and greater numbers of layers increasing model performance.

Conclusions

Given the sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition, out results indicate the need for increased attention to landscape definition in future studies.

     

Making the connection: combining habitat suitability and landscape connectivity to understand species distribution in an agricultural landscape

Landscape Ecology - The current biodiversity crisis has intensified the need to predict species responses to landscape modification and has renewed attention on the fundamental question of what...     

Temporal shifts in landscape connectivity for an ecosystem engineer,the roe deer,across a multiple-use landscape

Context

Routine movements of large herbivores, often considered as ecosystem engineers, impact key ecological processes. Functional landscape connectivity for such species influences the spatial distribution of associated ecological services and disservices.

Objectives

We studied how spatio-temporal variation in the risk-resource trade-off, generated by fluctuations in human activities and environmental conditions, influences the routine movements of roe deer across a heterogeneous landscape, generating shifts in functional connectivity at daily and seasonal time scales.

Methods

We used GPS locations of 172 adult roe deer and step selection functions to infer landscape connectivity. In particular, we assessed the influence of six habitat features on fine scale movements across four biological seasons and three daily periods, based on variations in the risk-resource trade-off.

Results

The influence of habitat features on roe deer movements was strongly dependent on proximity to refuge habitat, i.e. woodlands. Roe deer confined their movements to safe habitats during daytime and during the hunting season, when human activity is high. However, they exploited exposed open habitats more freely during night-time. Consequently, we observed marked temporal shifts in landscape connectivity, which was highest at night in summer and lowest during daytime in autumn. In particular, the onset of the autumn hunting season induced an abrupt decrease in landscape connectivity.

Conclusions

Human disturbance had a strong impact on roe deer movements, generating pronounced spatio-temporal variation in landscape connectivity. However, high connectivity at night across all seasons implies that Europe’s most abundant and widespread large herbivore potentially plays a key role in transporting ticks, seeds and nutrients among habitats.

     

Habitat suitability modelling to correlate gene flow with landscape connectivity

Landscape connectivity is important in designing corridor and reserve networks. Combining genetic distances among individuals with least-cost path (LCP) modelling helps to correlate indirect measures of gene flow with landscape connectivity. Applicability of LCP modelling, however, is reduced if knowledge on dispersal pathways or routes is lacking. Therefore, we integrated habitat suitability modelling into LCP analysis to avoid the subjectivity common in LCP analyses lacking knowledge on dispersal pathways or routes. We used presence-only data and ecological niche factor analysis to model habitat suitability for the spiny rat, Niviventer coninga, in a fragmented landscape of western Taiwan. We adapted the resultant habitat suitability map for incorporation into LCP analyses. Slightly increased Mantel correlations indicated that a class-weighted suitability map better explained genetic distances among individuals than did geographical distances. The integration of habitat suitability modelling into LCP analysis can thus generate information on distribution of suitable habitats, on potential routes of dispersal, for placement of corridors, and evaluate landscape connectivity. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Limiting factors and landscape connectivity: the American marten in the Rocky Mountains

In mobile animals, movement behavior can maximize fitness by optimizing access to critical resources and minimizing risk of predation. We sought to evaluate several hypotheses regarding the effects of landscape structure on American marten foraging path selection in a landscape experiencing forest perforation by patchcut logging. We hypothesized that in the uncut pre-treatment landscape marten would choose foraging paths to maximize access to cover types that support the highest density of prey. In contrast, in the post-treatment landscapes we hypothesized marten would choose paths primarily to avoid crossing openings, and that this would limit their ability to optimally select paths to maximize foraging success. Our limiting factor analysis shows that different resistant models may be supported under changing landscape conditions due to threshold effects, even when a species’ response to landscape variables is constant. Our results support previous work showing forest harvest strongly affects marten movement behavior. The most important result of our study, however, is that the influence of these features changes dramatically depending on the degree to which timber harvest limits available movement paths. Marten choose foraging paths in uncut landscapes to maximize time spent in cover types providing the highest density of prey species. In contrast, following landscape perforation by patchcuts, marten strongly select paths to avoid crossing unforested areas. This strong response to patch cutting reduces their ability to optimize foraging paths to vegetation type. Marten likely avoid non-forested areas in fragmented landscapes to reduce risk of predation and to benefit thermoregulation in winter, but in doing so they may suffer a secondary cost of decreased foraging efficiency.