Predicting loss and fragmentation of habitat of the vulnerable subtropical rainforest tree Macadamia integrifolia with models developed from compiled ecological data

Habitat models are widely used in ecology, however there are relatively few studies of rare species, primarily because of a paucity of survey records and lack of robust means of assessing accuracy of modelled spatial predictions. We investigated the potential of compiled ecological data in developing habitat models for Macadamia integrifolia, a vulnerable mid-stratum tree endemic to lowland subtropical rainforests of southeast Queensland, Australia. We compared performance of two binomial models—Classification and Regression Trees (CART) and Generalised Additive Models (GAM)—with Maximum Entropy (MAXENT) models developed from (i) presence records and available absence data and (ii) developed using presence records and background data. The GAM model was the best performer across the range of evaluation measures employed, however all models were assessed as potentially useful for informing in situ conservation of M. integrifolia, A significant loss in the amount of M. integrifolia habitat has occurred (p < 0.05), with only 37% of former habitat (pre-clearing) remaining in 2003. Remnant patches are significantly smaller, have larger edge-to-area ratios and are more isolated from each other compared to pre-clearing configurations (p < 0.05). Whilst the network of suitable habitat patches is still largely intact, there are numerous smaller patches that are more isolated in the contemporary landscape compared with their connectedness before clearing. These results suggest that in situ conservation of M. integrifolia may be best achieved through a landscape approach that considers the relative contribution of small remnant habitat fragments to the species as a whole, as facilitating connectivity among the entire network of habitat patches.

Risk of Agricultural Practices and Habitat Change to Farmland Birds

Many common bird species have declined as a result of agricultural intensification and this could be mitigated by organic farming. We paired sites for habitat and geographical location on organic and nonorganic farms in Ontario, Canada to test a priori predictions of effects on birds overall, 9 guilds and 22 species in relation to candidate models for farming practices (13 variables), local habitat features (12 variables), or habitat features that influence susceptibility to predation. We found that: (1) Overall bird abundance, but not richness, was significantly (p < 0.05) higher on organic sites (mean 43.1 individuals per site) than nonorganic sites (35.8 individuals per site). Significantly more species of birds were observed for five guilds, including primary grassland birds, on organic vs. nonorganic sites. No guild had higher richness or abundance on nonorganic farms; (2) Farming practice models were the best (ΔAIC < 4) for abundance of birds overall, primary grassland bird richness, sallier aerial insectivore richness and abundance, and abundance of ground nesters; (3) Habitat models were the best for overall richness, Neotropical migrant abundance, richness and abundance of Ontario-USA-Mexico (short-distance) migrants and resident richness; (4) Predation models were the best for richness of secondary grassland birds and ground feeders; (5) A combination of variables from the model types were best for richness or abundance overall, 13 of 18 guilds (richness and abundance) and 16 of 22 species analyzed. Five of 10 farming practice variables (including herbicide use, organic farm type) and 9 of 13 habitat variables (including hedgerow length, proportion of hay) were significant in best models. Risk modeling indicated that herbicide use could decrease primary grassland birds by one species (35% decline from 3.4 to 2.3 species) per site. Organic farming could benefit species of conservation concern by 49% (an increase from 7.6 to 11.4 grassland birds). An addition of 63 m of hedgerow could increase abundance and richness of short distance migrants by 50% (3.0 to 4.8 and 1.3 to 2.0, respectively). Increasing the proportion of hay on nonorganic farms to 50% could increase abundance of primary grassland bird by 40% (6.7 to 9.4). Our results provide support for alternative farmland designs and agricultural management systems that could enhance select bird species in farmland.

Modeling Habitat Associations for the Common Loon (Gavia immer) at Multiple Scales in Northeastern North America

Common Loon (Gavia immer) is considered an emblematic and ecologically important example of aquatic-dependent wildlife in North America. The northern breeding range of Common Loon has contracted over the last century as a result of habitat degradation from human disturbance and lakeshore development. We focused on the state of New Hampshire, USA, where a long-term monitoring program conducted by the Loon Preservation Committee has been collecting biological data on Common Loon since 1976. The Common Loon population in New Hampshire is distributed throughout the state across a wide range of lake-specific habitats, water quality conditions, and levels of human disturbance. We used a multiscale approach to evaluate the association of Common Loon and breeding habitat within three natural physiographic ecoregions of New Hampshire. These multiple scales reflect Common Loon-specific extents such as territories, home ranges, and lake-landscape influences. We developed ecoregional multiscale models and compared them to single-scale models to evaluate model performance in distinguishing Common Loon breeding habitat. Based on information-theoretic criteria, there is empirical support for both multiscale and single-scale models across all three ecoregions, warranting a model-averaging approach. Our results suggest that the Common Loon responds to both ecological and anthropogenic factors at multiple scales when selecting breeding sites. These multiscale models can be used to identify and prioritize the conservation of preferred nesting habitat for Common Loon populations.

Golden-winged Warbler habitat model validation for northern Wisconsin and central Minnesota

Between 1966 and 2003, the Golden-winged Warbler (Vermivora chrysoptera) experienced declines of 3.4% per year in large parts of the breeding range and has been identified by Partners in Flight as one of 28 land birds requiring expedient action to prevent its continued decline. It is currently being considered for listing under the Endangered Species Act. A major step in advancing our understanding of the status and habitat preferences of Golden-winged Warbler populations in the Upper Midwest was initiated by the publication of new predictive spatially explicit Golden-winged Warbler habitat models for the northern Midwest. Here, I use original data on observed Golden-winged Warbler abundances in Wisconsin and Minnesota to compare two population models: the hierarchical spatial count (HSC) model with the Habitat Suitability Index (HSI) model. I assessed how well the field data compared to the model predictions and found that within Wisconsin, the HSC model performed slightly better than the HSI model whereas both models performed relatively equally in Minnesota. For the HSC model, I found a 10% error of commission in Wisconsin and a 24.2% error of commission for Minnesota. Similarly, the HSI model has a 23% error of commission in Minnesota; in Wisconsin due to limited areas where the HSI model predicted absences, there was incomplete data and I was unable to determine the error of commission for the HSI model. These are sites where the model predicted presences and the Golden-winged Warbler did not occur. To compare predicted abundance from the two models, a 3x3 contingency table was used. I found that when overlapped, the models do not complement one another in identifying Golden-winged Warbler presences. To calculate discrepancy between the models, the error of commission shows that the HSI model has only a 6.8% chance of correctly classifying absences in the HSC model. The HSC model has only 3.3% chance of correctly classifying absences in the HSI model. These findings highlight the importance of grasses for nesting, shrubs used for cover and foraging, and trees for song perches and foraging as key habitat characteristics for breeding territory occupancy by singing males.

Uncertain species distribution: From interpolation to extrapolation

Species distribution models (SDMs) are considered to exemplify Pattern rather than Process based models of a species' response to its environment. Hence when used to map species distribution, the purpose of SDMs can be viewed as interpolation, since species response is measured at a few sites in the study region, and the aim is to interpolate species response at intermediate sites. Increasingly, however, SDMs are also being used to also extrapolate species-environment relationships beyond the limits of the study region as represented by the training data. Regardless of whether SDMs are to be used for interpolation or extrapolation, the debate over how to implement SDMs focusses on evaluating the quality of the SDM, both ecologically and mathematically. This paper proposes a framework that includes useful tools previously employed to address uncertainty in habitat modelling. Together with existing frameworks for addressing uncertainty more generally when modelling, we then outline how these existing tools help inform development of a broader framework for addressing uncertainty, specifically when building habitat models. As discussed earlier we focus on extrapolation rather than interpolation, where the emphasis on predictive performance is diluted by the concerns for robustness and ecological relevance. We are cognisant of the dangers of excessively propagating uncertainty. Thus, although the framework provides a smorgasbord of approaches, it is intended that the exact menu selected for a particular application, is small in size and targets the most important sources of uncertainty. We conclude with some guidance on a strategic approach to identifying these important sources of uncertainty. Whilst various aspects of uncertainty in SDMs have previously been addressed, either as the main aim of a study or as a necessary element of constructing SDMs, this is the first paper to provide a more holistic view.

Unveiling the factors shaping the distribution of widely distributed alpine vertebrates, using multi-scale ecological niche modelling of the bat Plecotus macrobullaris

Several alpine vertebrates share a distribution pattern that extends across the South-western Palearctic but is limited to the main mountain massifs. Although they are usually regarded as cold-adapted species, the range of many alpine vertebrates also includes relatively warm areas, suggesting that factors beyond climatic conditions may be driving their distribution. In this work we first recognize the species belonging to the mentioned biogeographic group and, based on the environmental niche analysis of Plecotus macrobullaris, we identify and characterize the environmental factors constraining their ranges. Distribution overlap analysis of 504 European vertebrates was done using the Sorensen Similarity Index, and we identified four birds and one mammal that share the distribution with P. macrobullaris. We generated 135 environmental niche models including different variable combinations and regularization values for P. macrobullaris at two different scales and resolutions. After selecting the best models, we observed that topographic variables outperformed climatic predictors, and the abruptness of the landscape showed better predictive ability than elevation. The best explanatory climatic variable was mean summer temperature, which showed that P. macrobullaris is able to cope with mean temperature ranges spanning up to 16 degrees C. The models showed that the distribution of P. macrobullaris is mainly shaped by topographic factors that provide rock-abundant and open-space habitats rather than climatic determinants, and that the species is not a cold-adapted, but rather a cold-tolerant eurithermic organism. P. macrobullaris shares its distribution pattern as well as several ecological features with five other alpine vertebrates, suggesting that the conclusions obtained from this study might be extensible to them. We concluded that rock-dwelling and open-space foraging vertebrates with broad temperature tolerance are the best candidates to show wide alpine distribution in the Western Palearctic.

Modelling the future biogeography of North Atlantic zooplankton communities in response to climate change

Advances in habitat and climate modelling allow us to reduce uncertainties of climate change impacts on species distribution. We evaluated the impacts of future climate change on community structure, diversity, distribution and phenology of 14 copepod species in the North Atlantic. We developed and validated habitat models for key zooplankton species using continuous plankton recorder (CPR) survey data collected at mid latitudes of the North Atlantic. Generalized additive models (GAMs) were applied to relate the occurrence of species to environmental variables. Models were projected to future (2080–2099) environmental conditions using coupled hydroclimatix–biogeochemical models under the Intergovernmental Panel on Climate Change (IPCC) A1B climate scenario, and compared to present (2001–2020) conditions. Our projections indicated that the copepod community is expected to respond substantially to climate change: a mean poleward latitudinal shift of 8.7 km per decade for the overall community with an important species range variation (–15 to 18 km per decade); the species seasonal peak is expected to occur 12–13 d earlier for Calanus finmarchicus and C. hyperboreus; and important changes in community structure are also expected (high species turnover of 43–79% south of the Oceanic Polar Front). The impacts of the change expected by the end of the century under IPCC global warming scenarios on copepods highlight poleward shifts, earlier seasonal peak and changes in biodiversity spatial patterns that might lead to alterations of the future North Atlantic pelagic ecosystem. Our model and projections are supported by a temporal validation undertaken using the North Atlantic climate regime shift that occurred in the 1980s: the habitat model built in the cold period (1970–1986) has been validated in the warm period (1987–2004).

Scenario-based assessment of future land use change on butterfly species distributions

Species distribution models (SDMs) are increasingly used to predict environmentally induced range shifts of habitats of plant and animal species. Consequently SDMs are valuable tools for scientifically based conservation decisions. The aims of this paper are (1) to identify important drivers of butterfly species persistence or extinction, and (2) to analyse the responses of endangered butterfly species of dry grasslands and wetlands to likely future landscape changes in Switzerland. Future land use was represented by four scenarios describing: (1) ongoing land use changes as observed at the end of the last century; (2) a liberalisation of the agricultural markets; (3) a slightly lowered agricultural production; and (4) a strongly lowered agricultural production. Two model approaches have been applied. The first (logistic regression with principal components) explains what environmental variables have significant impact on species presence (and absence). The second (predictive SDM) is used to project species distribution under current and likely future land uses. The results of the explanatory analyses reveal that four principal components related to urbanisation, abandonment of open land and intensive agricultural practices as well as two climate parameters are primary drivers of species occurrence (decline). The scenario analyses show that lowered agricultural production is likely to favour dry grassland species due to an increase of non-intensively used land, open canopy forests, and overgrown areas. In the liberalisation scenario dry grassland species show a decrease in abundance due to a strong increase of forested patches. Wetland butterfly species would decrease under all four scenarios as their habitats become overgrown

Forest Policy Scenario Analysis: Sensitivity of Songbird Community to Changes in Forest Cover Amount and Configuration

Changes in mature forest cover amount, composition, and configuration can be of significant consequence to wildlife populations. The response of wildlife to forest patterns is of concern to forest managers because it lies at the heart of such competing approaches to forest planning as aggregated vs. dispersed harvest block layouts. In this study, we developed a species assessment framework to evaluate the outcomes of forest management scenarios on biodiversity conservation objectives. Scenarios were assessed in the context of a broad range of forest structures and patterns that would be expected to occur under natural disturbance and succession processes. Spatial habitat models were used to predict the effects of varying degrees of mature forest cover amount, composition, and configuration on habitat occupancy for a set of 13 focal songbird species. We used a spatially explicit harvest scheduling program to model forest management options and simulate future forest conditions resulting from alternative forest management scenarios, and used a process-based fire-simulation model to simulate future forest conditions resulting from natural wildfire disturbance. Spatial pattern signatures were derived for both habitat occupancy and forest conditions, and these were placed in the context of the simulated range of natural variation. Strategic policy analyses were set in the context of current Ontario forest management policies. This included use of sequential time-restricted harvest blocks (created for Woodland caribou (Rangifer tarandus) conservation) and delayed harvest areas (created for American marten (Martes americana atrata) conservation). This approach increased the realism of the analysis, but reduced the generality of interpretations. We found that forest management options that create linear strips of old forest deviate the most from simulated natural patterns, and had the greatest negative effects on habitat occupancy, whereas policy options that specify deferment and timing of harvest for large blocks helped ensure the stable presence of an intact mature forest matrix over time. The management scenario that focused on maintaining compositional targets best supported biodiversity objectives by providing the composition patterns required by the 13 focal species, but this scenario may be improved by adding some broad-scale spatial objectives to better maintain large blocks of interior forest habitat through time.

Modeling and Mapping Golden-winged Warbler Abundance to Improve Regional Conservation Strategies

Conservation planning requires identifying pertinent habitat factors and locating geographic locations where land management may improve habitat conditions for high priority species. I derived habitat models and mapped predicted abundance for the Golden-winged Warbler (Vermivora chrysoptera), a species of high conservation concern, using bird counts, environmental variables, and hierarchical models applied at multiple spatial scales. My aim was to understand habitat associations at multiple spatial scales and create a predictive abundance map for purposes of conservation planning for the Golden-winged Warbler. My models indicated a substantial influence of landscape conditions, including strong positive associations with total forest composition within the landscape. However, many of the associations I observed were counter to reported associations at finer spatial extents; for instance, I found Golden-winged Warblers negatively associated with several measures of edge habitat. No single spatial scale dominated, indicating that this species is responding to factors at multiple spatial scales. I found Golden-winged Warbler abundance was negatively related with Blue-winged Warbler (Vermivora cyanoptera) abundance. I also observed a north-south spatial trend suggestive of a regional climate effect that was not previously noted for this species. The map of predicted abundance indicated a large area of concentrated abundance in west-central Wisconsin, with smaller areas of high abundance along the northern periphery of the Prairie Hardwood Transition. This map of predicted abundance compared favorably with independent evaluation data sets and can thus be used to inform regional planning efforts devoted to conserving this species.

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.