Rural housing is related to plant invasions in forests of southern Wisconsin,USA

Forests throughout the US are invaded by non-native invasive plants. Rural housing may contribute to non-native plant invasions by introducing plants via landscaping, and by creating habitat conditions favorable for invaders. The objective of this paper was to test the hypothesis that rural housing is a significant factor explaining the distribution of invasive non-native plants in temperate forests of the Midwestern US. In the Baraboo Hills, Wisconsin, we sampled 105 plots in forest interiors. We recorded richness and abundance of the most common invasive non-native plants and measured rural housing, human-caused landscape fragmentation (e.g. roads and forest edges), forest structure and topography. We used regression analysis to identify the variables more related to the distribution of non-native invasive plants (best subset and hierarchical partitioning analyses for richness and abundance and logistic regression for presence/absence of individual species). Housing variables had the strongest association with richness of non-native invasive plants along with distance to forest edge and elevation, while the number of houses in a 1 km buffer around each plot was the variable most strongly associated with abundance of non-native invasive plants. Rhamnus cathartica and Lonicera spp. were most strongly associated with rural housing and fragmentation. Berberis thumbergii and Rosa multiflora were associated with gentle slopes and low elevation, while Alliaria petiolata was associated with higher cover of native vegetation and stands with no recent logging history. Housing development inside or adjacent to forests of high conservation value and the use of non-native invasive plants for landscaping should be discouraged.     

Temporal change in fragmentation of continental US forests

Changes in forest ecosystem function and condition arise from changes in forest fragmentation. Previous studies estimated forest fragmentation for the continental United States (US). In this study, new temporal land-cover data from the National Land Cover Database (NLCD) were used to estimate changes in forest fragmentation at multiple scales for the continental US. Early and late dates for the land-cover change data were ca. 1992 and ca. 2001. Forest density was used as a multi-scale index of fragmentation by measuring the proportion of forest in neighborhoods ranging in size from 2.25 to 5314.41 ha. The multi-scale forest density maps were classified using thresholds of 40% (patch), 60% (dominant), and 90% (interior) to analyze temporal change of fragmentation. The loss of dominant and interior forest showed distinct scale effects, whereas loss of patch forest was much less scale-dependent. Dominant forest loss doubled from the smallest to the largest spatial scale, while interior forest loss increased by approximately 80% from the smallest to the second largest spatial scale, then decreased somewhat. At the largest spatial scale, losses of dominant and interior forest were 5 and 10%, respectively, of their ca. 1992 amounts. In contrast, patch forest loss increased by only 25% from the smallest to largest spatial scale. These results indicate that continental US forests were sensitive to forest loss because of their already fragmented state. Forest loss would have had to occur in an unlikely spatial pattern in order to avoid the proportionately greater impact on dominant and interior forest at larger spatial scales.     

Predicting the distributions of plant species at the regional scale: a hierarchical matrix model

This paper describes a model which links four levels in an ecological hierarchy using a series of matrices. The four levels are landscape, land cover type, community and species. Each matrix quantifies the probabilistic associations between entities in two adjacent levels in the hierarchy. A landscape classification (1 km resolution) provides a spatial element to the model enabling the distributions of species to be predicted and presented as maps within a geographical information system (GIS). Implementation of the model in Northern England is described. The distributions of 579 species of plants were predicted and compared with data from independent field surveys. The predicted distributions were found to be accurate for 59 % of species. The distributions of rare and non-native (introduced) species of plant were relatively poorly predicted. The potential of this approach to model plant species distributions is discussed.     

Landscape patterns of nitrogen mineralization and nitrification in southern Ohio hardwood forests

This study quantified nitrogen mineralization and nitrification potentials in soils of hardwood forests of southern Ohio at three spatial scales: (1) the regional scale, represented by four study areas of 90–120 ha separated by 3–65 km, (2) the local scale, represented by three contiguous watersheds within each study area, and (3) the topographic scale, represented by xeric, intermediate, and mesic sites within each watershed, as defined by a GIS-generated Integrated Moisture Index (IMI). Organic C, NO₃ pool size, net N mineralization, proportional nitrification, and net nitrification potentials all varied among study sites (i.e. at the regional scale). Using path analysis, we were able to construct scale-independent causal models explaining 30–35% of the variance in organic C and potential net N mineralization and 70% of the variance in potential net NO₃ production. Site- and scale-specific differences in geology and/or land use history among study sites were likely responsible for the variation not explained by the path analysis. At the local scale, there were significant variations in organic C and inorganic N pool sizes among watersheds within a study site in two of the four study sites. In addition, most parameters we measured varied significantly along the topographic gradient (i.e. with long-term soil moisture availability/IMI). Based on our results, scaling up models of nitrification from plot scale to the regional scale should be straightforward, whereas scaling up organic C storage and N mineralization will require incorporation of independent scaling paradigms at three (or more) spatial scales.     

Gradient modeling of conifer species using random forests

Landscape ecology often adopts a patch mosaic model of ecological patterns. However, many ecological attributes are inherently continuous and classification of species composition into vegetation communities and discrete patches provides an overly simplistic view of the landscape. If one adopts a niche-based, individualistic concept of biotic communities then it may often be more appropriate to represent vegetation patterns as continuous measures of site suitability or probability of occupancy, rather than the traditional abstraction into categorical community types represented in a mosaic of discrete patches. The goal of this paper is to demonstrate the high effectiveness of species-level, pixel scale prediction of species occupancy as a continuous landscape variable, as an alternative to traditional classified community type vegetation maps. We use a Random Forests ensemble learning approach to predict site-level probability of occurrence for four conifer species based on climatic, topographic and spectral predictor variables across a 3,883 km² landscape in northern Idaho, USA. Our method uses a new permutated sample-downscaling approach to equalize sample sizes in the presence and absence classes, a model selection method to optimize parsimony, and independent validation using prediction to 10% bootstrap data withhold. The models exhibited very high accuracy, with AUC and kappa values over 0.86 and 0.95, respectively, for all four species. The spatial predictions produced by the models will be of great use to managers and scientists, as they provide vastly more accurate spatial depiction of vegetation structure across this landscape than has previously been provided by traditional categorical classified community type maps.     

Testing assumptions of cost surface analysis—a tool for invasive species management

Applied ecology could benefit from new tools that identify potential movement pathways of invasive species, particularly where data are sparse. Cost surface analysis (CSA) estimates the permeability (friction) across a landscape and can be applied to dispersal modelling. Increasingly used in a diversity of applications, several fundamental assumptions that might influence the outputs of CSA (cost surfaces and least-cost pathways) have yet to be systematically examined. Thus, we explore two issues: the presumed relationship between habitat preferences and dispersal behaviour as well as the degree of landscape fragmentation through which an organism moves by modelling a total of 18 sensitivity and dispersal scenarios. We explored the effect of fragmentation by altering the friction values (generally assigned using expert opinion) associated with patch and linear features. We compared these sensitivity scenarios in two sites that differed in fragmentation. We also used eastern grey squirrels (Sciurus carolinensis) as an example invading species and compared diffusion models and two contrasting cost surface dispersal scenarios. The diffusion model underestimated spread because squirrels did not move randomly through the landscape. Despite contrasting assumptions regarding dispersal behaviour, the two cost surfaces were strikingly similar while the least-cost paths differed. Furthermore, while the cost surfaces were insensitive to changes in friction values for linear features, they were sensitive to assumptions made for patch features. Our results suggest that movement in fragmented landscapes may be more sensitive to assumptions regarding friction values than contiguous landscapes. Thus, the reliability of CSA may depend not only on the range of friction values used for patches but also the degree of contiguity in the landscape.     

A tale of two wildfires; testing detection and prediction of invasive species distributions using models fit with topographic and spectral indices

Context

Developing species distribution models (SDMs) to detect invasive species cover and evaluate habitat suitability are high priorities for land managers.

Objectives

We tested SDMs fit with different variable combinations to provide guidelines for future invasive species model development based on transferability between landscapes.

Methods

Generalized linear model, boosted regression trees, multivariate adaptive regression splines, and Random Forests were fit with location data for high cheatgrass (Bromus tectorum) cover in situ for two post-burn sites independently using topographic indices, spectral indices derived from multiple dates of Landsat 8 satellite imagery, or both. Models developed for one site were applied to the other, using independent cheatgrass cover data from the respective ex situ site to test model transferability.

Results

Fitted models were statistically robust and comparable when fit with at least 200 cover plots in situ and transferred to the ex situ site. Only the Random Forests models were robust when fit with a small number of cover plots in situ.

Conclusions

Our study indicated spectral indices can be used in SDMs to estimate species cover across landscapes (e.g., both within the same Landsat scene and in an adjacent Landsat scene). Important considerations for transferability include the model employed, quantity of cover data used to train/test the models, and phenology of the species coupled with the timing of imagery. The results also suggest that when cover data are limited, SDMs fit with topographic indices are sufficient for evaluating cheatgrass habitat suitability in new post-disturbance landscapes; however, spectral indices can provide a more robust estimate for detection based on local phenology.

     

The importance of land-use legacies for modeling present-day species distributions

Context

Land-use legacies play an important role in shaping contemporary species distributions. However, land-use legacies are rarely considered in species distribution models (SDMs) that aim to model present-day species distributions across the landscape, even though they can lead to a species absence in suitable areas. SDMs that do not account for land-use legacies will likely result in biased predictions of species distributions.

Objective

We examine the importance of land-use legacies for modeling present-day distributions of tree species at a regional scale, assessing how the addition of land-use legacy variables improves predictive power of SDMs.

Methods

We generated land-use legacy variables using raster layers of reconstructed historical agricultural land use and 3310 inventory plots. SDMs were developed for six forest tree species based on climatic, edaphic, and topographic variables, and with (SDM_LU) and without (SDM_Base) land-use legacy variables. We compared the predictive power between SDM_LU and SDM_Base models and then quantified the local importance of land-use legacy variables relative to other abiotic variables.

Results

Our results show that the importance of land-use legacy variables for present-day species distributions and the improvement on the predictive power of SDMs is species-specific. The inclusion of land-use legacy variables improved SDMs primarily by lowering errors of commission and increasing the overall accuracy of prediction.

Conclusion

The influence of land-use legacies on SDMs suggests that, for some tree species, incorporating land-use legacies can accurately identify suitable areas that are not occupied by the species due to land-use legacies, and advance our understanding of their present-day distributions.

     

The distribution of plant species in urban vegetation fragments

(1) The presence and absence of 22 plant species of various growth forms and habitat associations were analysed in 423 habitat fragments totalling 10.4 km² in a 268 km² urban and suburban region, in Birmingham, UK. (2) Multivariate logistic regressions were used to assess the effects of patch geometry and quality on the species distributions. Measures of geometry were area, shape (S-factor), distance from open countryside and various measures of isolation from other patches. Potential habitat for each species was determined quantitatively, and the distribution of each species was considered within a subset of patches containing potentially suitable habitat types. There was found to be a significant positive correlation between the density of patches available to a species and the proportion of these patches which were occupied. (3) Logistic analyses and incidence functions revealed that, for many of the species, occupancy increased with site age, area, habitat number and similarity of adjacent habitats, while increasing distance to the nearest recorded population of the same species decreased the likelihood that a species would be found in a patch. (4) Patterns of occupancy are consistent with increased extinction from small sites, and colonisation of nearby habitats, coupled with an important role for site history. We conclude that spatial dynamics at the scale of the landscape are of importance to the long-term persistence of many plant species in fragmented landscapes, and must be seriously considered in conservation planning and management. These results have direct implications for the siting and connectivity of urban habitat reserves.     

Potential colonization of newly available tree-species habitat under climate change: An analysis for five eastern US species

We used a combination of two models, DISTRIB and SHIFT, to estimate potential migration of five tree species into suitable habitat due to climate change over the next 100 years. These species, currently confined to the eastern half of the United States and not extending into Canada, are Diospyros virginiana (persimmon), Liquidambar styraciflua (sweetgum), Oxydendrum arboreum (sourwood), Pinus taeda (loblolly pine), and Quercus falcata var. falcata (southern red oak). DISTRIB uses a statistical approach to assess potential suitable habitat under equilibrium of 2 × CO₂. SHIFT uses a cellular automata approach to estimate migration and is driven primarily by the abundance of the species near the boundary, forest density inside and outside of the boundary, and distance between cells. For each cell outside the current boundary, SHIFT creates an estimate of the probability that each unoccupied target cell will become colonized over 100 years. By evaluating the probability of colonization within the potential ‘new’ suitable habitat, we can estimate the proportion of new habitat that might be colonized within a century. This proportion is low (<15%) for all five species, suggesting that there is a serious lag between the potential movement of suitable habitat and the potential for the species to migrate into the new habitat. However, humans could hasten the migration of certain species by physically moving the propagules, especially for certain rare species that are unable to move sufficiently through fragmented landscapes, or even more common species, e.g., beech, that have lost many of their animal dispersers.     

High-elevation landforms limit the movement of invasive small mammal species

Landscape Ecology - Large-scale programs for eradication of pest mammals are confronted with the challenge of managing reinvasion. Exploiting high-elevation landscape features that naturally limit...     

Comparing climate-growth responses of urban and non-urban forests using L. tulipifera tree-rings in southern Indiana,USA

Urban forests have many positive effects on human health and recreation. However, urban areas can create stressful environments for native trees, leading to increased mortality and an altered ecosystem. Here, we compare growth variability and the climate response from old (>200 years) L. tulipifera growing in an urban forest in Bloomington, IN to surrounding non-urban sites in southern Indiana using dendrochronological techniques. We found that L. tulipifera growing in the urban forest responded similarly with small differences to climate compared to the non-urban sites. Radial growth from urban L. tulipifera had statistically similar correlation values with temperature, soil moisture, and precipitation compared to the trees in non-urban forests. Growth variability between the urban and non-urban L. tulipifera trees showed good agreement through time with the exception of the 20th century, where the urban forest experienced a stand-wide release from competition. Our results indicate that some urban forests may function similarly to non-urban forests from an ecological perspective. These findings suggest management practices from non-urban old-growth forest could be useful for management of rare urban old-growth forests.     

Additive partitioning of plant species diversity in an agricultural mosaic landscape

In this paper, we quantify the effects of habitat variability and habitat heterogeneity based on the partitioning of landscape species diversity into additive components and link them to patch-specific diversity. The approach is illustrated with a case study from central Switzerland, where we recorded the presence of vascular plant species in a stratified random sample of 1'280 quadrats of 1 m² within a total area of 0.23 km². We derived components of within- and between-community diversity at four scale levels (quadrat, patch, habitat type, and landscape) for three diversity measures (species richness, Shannon index, and Simpson diversity). The model implies that what we measure as within-community diversity at a higher scale level is the combined effect of heterogeneity at various lower levels. The results suggest that the proportions of the individual diversity components depend on the habitat type and on the chosen diversity aspect. One habitat type may be more diverse than another at patch level, but less diverse at the level of habitat type. Landscape composition apparently is a key factor for explaining landscape species richness, but affects evenness only little. Before we can test the effect of landscape structure on landscape species richness, several problems will have to be solved. These include the incorporation of neighbourhood effects, the unbiased estimation of species richness components, and the quantification of the contribution of a landscape element to landscape species richness.     

Estimation and prediction of plant species richness in a mosaic landscape

Traditional agricultural mosaic landscapes are likely to undergo dramatic changes through either intensification or abandonment of land use. Both developmental trends may negatively affect the vascular plant species richness of such landscapes. Therefore, sustainable land-use systems need to be developed to maintain and re-establish species richness at various spatial scales. To evaluate the sustainability of specific land-use systems, we need approaches for the effective assessment of the present species richness and models that can predict the effects on species richness as realistically as possible. In this context, we present a methodology to estimate and predict vascular plant species richness at the local and the regional scale. In our approach, the major determinants of vascular plant species richness within the study area are taken into consideration: These are according to Duelli's mosaic concept the number of habitat types and of habitat patches within area units. Furthermore, it is based on the relative frequencies of species within habitat types. Our approach comprises six steps: (i) the determination of present habitat patterns within an observation area, (ii) the creation of a land-use scenario with simulated habitat patterns, (iii) the determination of species frequencies within habitat types of this area, (iv) a grouping of habitat-specific species, (v) the estimation of the probabilities for all species (or habitat specialists) to occur, either in stepwise, exponentially enlarged landscape tracts (local scale), or in the entire observation area (regional scale), and (vi) the validation of the estimated species numbers. The approach will be exemplified using data from the municipal district of Erda, Lahn-Dill Highlands, Germany. The current species numbers to be expected on the basis of probability calculations were compared with those recorded on the basis of extensive field work. This comparison shows that, on the basis of our simple calculations, the current local plant species richness can be predicted well, with a slight underestimation. This revised version was published online in May 2005 with corrections to the Cover Date. This revised version was published online in July 2006 with corrections to the Cover Date.     

Landscape analysis of plant diversity

Studies to identify gaps in the protection of habitat for speciesof concern have been inconclusive and hampered by single-scale orpoor multi-scale sampling methods, large minimum mapping units(MMU's of 2 ha to 100 ha), limited and subjectively selected fieldobservations, and poor mathematical and ecological models. Weovercome these obstacles with improved multi-scale samplingtechniques, smaller MMU's (< 0.02 ha), an unbiased sampling designbased on double sampling, improved mathematical models includingspecies-area curves corrected for habitat heterogeneity, andgeographic information system-based ecological models. We applythis landscape analysis approach to address resource issues inRocky Mountain National Park, Colorado. Specifically, we quantifythe effects of elk grazing on plant diversity, identify areas ofhigh or unique plant diversity needing increased protection, andevaluate the patterns of non-native plant species on thelandscape.Double sampling techniques use satellite imagery,aerial photography, and field data to stratify homogeneous andheterogeneous units and keystone ecosystems (ecosystems thatcontain or support a high number of species or have distinctivespecies compositions). We show how a multi-scale vegetationsampling design, species-area curves, analyses of within- andbetween-vegetation type species overlap, and geographic informationsystem (GIS) models can be used to quantify landscape-scalepatterns of vascular plant diversity in the Park.The new multi-scale vegetation plot techniques quickly differentiated plantspecies differences in paired study sites. Three plots in the OuzelBurn area (burned in 1978) contained 75 plant species, while only17 plant species were found in paired plots outside the burn.Riparian areas contained 109 plant species, compared to just 55species in paired plots in adjacent forests. However, plant speciesrichness patterns inside and outside elk exclosures were morecomplex. One elk exclosure contained more species than its adjacentopen range (52 species inside and 48 species outside). Two elkexclosures contained fewer species inside than outside (105 and 41species inside and 112 and 74 species outside, respectively).However, there was only 26% to 48% overlap (using Jaccard'sCoefficient) of plant species composition inside and outside theexclosures. One elk exclosure had 13% cover of non-indigenousspecies inside the exclosure compared to 4% outside, butnon-indigenous species cover varied by location.We compared plantdiversity patterns from vegetation maps made with 100 ha, 50 ha, 2ha, and 0.02 ha MMU's in the 754 ha Beaver Meadows study area usingfour 0.025 ha and twenty-one 0.1 ha multi-scale vegetation plots.Preliminary data suggested that the 2 ha MMU provided an accurateestimate of the number of plant species (–14%) for a study area,but the number of habitats (polygons) was reduced by 67%, andaspen, a unique and important habitat type, was missed entirely. Wedescribe a hypothesis-driven approach to the design andimplementation of geospatial databases for local resourcemonitoring and ecosystem management.     

Modeling invasive species spread in complex landscapes: the case of potato moth in Ecuador

Tropical mountains have a long history of human occupation, and although vulnerable to biological invasions, have received minimal attention in the literature. Understanding invasive pest dynamics in socio-ecological, agricultural landscapes, like the tropical Andes, is a challenging but timely issue for ecologists as it may provide developing countries with new tools to face increasing threats posed by these organisms. In this work, road rehabilitation into a remote valley of the Ecuadorian Andes constituted a natural experiment to study the spatial propagation of an invasive potato tuber moth into a previously non-infested agricultural landscape. We used a cellular automaton to model moth spatio-temporal dynamics. Integrating real-world variables in the model allowed us to examine the relative influence of environmental versus social landscape heterogeneity on moth propagation. We focused on two types of anthropogenic activities: (1) the presence and spatial distribution of traditional crop storage structures that modify local microclimate, and (2) long-distance dispersal (LDD) of moths by human-induced transportation. Data from participatory monitoring of pest invasion into the valley and from a larger-scale field survey on the Ecuadorian Andes allowed us to validate our model against actual presence/absence records. Our simulations revealed that high density and a clumped distribution of storage structures had a positive effect on moth invasion by modifying the temperature of the landscape, and that passive, LDD enhanced moth invasion. Model validation showed that including human influence produced more precise and realistic simulations. We provide a powerful and widely applicable methodological framework that stresses the crucial importance of integrating the social landscape to develop accurate invasion models of pest dynamics in complex, agricultural systems.     

OUTENIQUA — A computer model to simulate succession in the mixed evergreen forests of the southern Cape,South Africa

A succession model for mixed evergreen forests of the southern Cape, South Africa, called OUTENIQUA, was developed based on one for subtropical rain forest in New South Wales, Australia. The model simulates the regeneration, growth and mortality on a 0.04 ha plot using an individual-tree based modeling approach to forest succession. The OUTENIQUA model was tested on its ability to simulate species dynamics of the forest stand used for its development, as well as on independent data from a neighboring stand and not used for the model derivation. The model is used as a research tool to summarize published and unpublished knowledge on the southern Cape forests and to highlight aspects where knowledge is insufficient. The development of the model represents a test of an individual-tree gap model as a simulation tool for use in management and directing research in subtropical and tropical forests.     

Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation

Woody invasive plants are an increasing component of the New England flora. Their success and geographic spread are mediated in part by landscape characteristics. We tested whether woody invasive plant richness was higher in landscapes with many forest edges relative to other forest types and explained land use/land cover and forest fragmentation patterns using socioeconomic and physical variables. Our models demonstrated that woody invasive plant richness was higher in landscapes with more edge forest relative to patch, perforated, and especially core forest types. Using spatially-explicit, hierarchical Bayesian, compositional data models we showed that infrastructure and physical factors, including road length and elevation range, and time-lagged socioeconomic factors, primarily population, help to explain development and forest fragmentation patterns. Our social–ecological approach identified landscape patterns driven by human development and linked them to increased woody plant invasions. Identifying these landscape patterns will aid ongoing efforts to use current distribution patterns to better predict where invasive species may occur in unsampled regions under current and future conditions.     

Multi-scale environmental heterogeneity as a predictor of plant species richness

Ecological theory predicts a positive influence of local-, landscape-, and regional-scale spatial environmental heterogeneity on local species richness. Therefore, knowing how heterogeneity measured at a variety of scales relates to local species richness has important implications for conservation of biological diversity. We took a statistical modeling approach to determine which metrics of heterogeneity measured at which scales were useful predictors of local species richness, and whether the heterogeneity-local richness relationship was always positive. Local plant species richness data came from 400-m² vegetation plots in North and South Carolina, USA. At each of four scales from within plots to across regions, we used either GIS or field data to calculate measures of heterogeneity from abiotic environmental variables, vegetation productivity data, and land cover classifications. Among all predictors at all scales, we found that no measure of heterogeneity was a better predictor of local richness than mean pH within plots. However, at scales larger than within plots, measures of heterogeneity were correlated most strongly with local richness, and each of the three classes of variables we used had a distinct scale at which it performed better than the others. These results highlight the fact that ecological processes occurring across multiple scales influence local species richness differently. In addition, relationships between heterogeneity and richness were usually, though not always, positive, underscoring the importance of processes that occur at a variety of scales to local biodiversity conservation and management.