Untangling associations between chironomid taxa in Neotropical streams using local and landscape filters

1. Analyses of species association have major implications for selecting indicators for freshwater biomonitoring and conservation, because they allow for the elimination of redundant information and focus on taxa that can be easily handled and identified. These analyses are particularly relevant in the debate about using speciose groups (such as the Chironomidae) as indicators in the tropics, because they require difficult and time-consuming analysis, and their responses to environmental gradients, including anthropogenic stressors, are poorly known. 2. Our objective was to show whether chironomid assemblages in Neotropical streams include clear associations of taxa and, if so, how well these associations could be explained by a set of models containing information from different spatial scales. For this, we formulated a priori models that allowed for the influence of local, landscape and spatial factors on chironomid taxon associations (CTA). These models represented biological hypotheses capable of explaining associations between chironomid taxa. For instance, CTA could be best explained by local variables (e.g. pH, conductivity and water temperature) or by processes acting at wider landscape scales (e.g. percentage of forest cover). 3. Biological data were taken from 61 streams in Southeastern Brazil, 47 of which were in well-preserved regions, and 14 of which drained areas severely affected by anthropogenic activities. We adopted a model selection procedure using Akaike`s information criterion to determine the most parsimonious models for explaining CTA. 4. Applying Kendall`s coefficient of concordance, seven genera (Tanytarsus/Caladomyia, Ablabesmyia, Parametriocnemus, Pentaneura, Nanocladius, Polypedilum and Rheotanytarsus) were identified as associated taxa. The best-supported model explained 42.6% of the total variance in the abundance of associated taxa. This model combined local and landscape environmental filters and spatial variables (which were derived from eigenfunction analysis). However, the model with local filters and spatial variables also had a good chance of being selected as the best model. 5. Standardised partial regression coefficients of local and landscape filters, including spatial variables, derived from model averaging allowed an estimation of which variables were best correlated with the abundance of associated taxa. In general, the abundance of the associated genera tended to be lower in streams characterised by a high percentage of forest cover (landscape scale), lower proportion of muddy substrata and high values of pH and conductivity (local scale). 6. Overall, our main result adds to the increasing number of studies that have indicated the importance of local and landscape variables, as well as the spatial relationships among sampling sites, for explaining aquatic insect community patterns in streams. Furthermore, our findings open new possibilities for the elimination of redundant data in the assessment of anthropogenic impacts on tropical streams.

Landscape characteristics associated with species richness and occurrence of small native mammals inhabiting a coastal heathland: a spatial modelling approach

In the coastal region of south-western Victoria, Australia, populations of native small mammal species are restricted to patches of suitable habitat in a highly fragmented landscape. The size and spatial arrangement of these patches is likely to influence both the occupancy and richness of species at a location. Geographic Information System (GIS)-based habitat models of the species richness of native small mammals, and individual species  occurrences, were developed to produce maps displaying the spatial  configuration of suitable habitat. Models were generated using either generalised linear Poisson regression (for species richness) or logistic regression (for species occurrences) with species richness or  presence/absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictor variables. A multi-model inference approach based on the Akaike Information Criterion was used and the resulting model was applied in a GIS framework to extrapolate predicted richness/likelihood of occurrence across the entire area of the study. A negative association between species  richness and elevation, habitat complexity and sun index indicated that richness within the study area decreases with increasing altitude, vertical vegetation structure and exposure to solar radiation. Landform  characteristics were important (to varying degrees) in determining habitat occupancy for all of the species examined, while the influence of habitat complexity was important for only one of the species. Performance of all but one of the models generated using presence/absence data was high, as indicated by the area under the curve of a receiver-operating characteristic plot. The effective conservation of the small mammal species in the area of concern is likely to depend on management actions that promote the protection of the critical habitats identified in the models.

The relative importance of landscape properties for woodland birds in agricultural environments

1. Studies of landscape change are seldom conducted at scales commensurate with the processes they purport to investigate. Landscape change is a landscape-level process, yet most studies focus on patches. Even when landscape context is considered, inference remains at the patch-level. The unit of investigation must be extended beyond individual patches to whole mosaics in order to advance understanding of faunal responses to landscape change.

2. In this study, we aggregated data from multiple sites per landscape such that both the response and explanatory variables characterized 'whole' landscapes, allowing for landscape-level inference about factors influencing species' incidence.

3. We used hierarchical partitioning and Bayesian variable selection methods to develop species-specific models that examined the influence of four categories of landscape properties – habitat extent, habitat configuration, landscape composition and geographical location – on the incidence of 58 species of woodland-dependent birds in 24 agricultural landscapes (each 100 km2) in south-eastern Australia.

4. There was strong evidence for a positive effect of habitat extent for 27 species. Thirty species were related to at least one of the four landscape composition variables, and geographical location was important for 19 species. Habitat configuration was influential for 13 species and where important, the impacts of fragmentation per se were detrimental.

5. Variation among species in the influential landscape variables indicates that different species respond to different sets of cues in land mosaics. Thus, although all species were grouped a priori as 'woodland-dependent', expectations based on general ecological characteristics may prove unreliable.

6. Synthesis and applications. These results underscore the value of moving beyond the fragmentation paradigm focused on the spatial pattern of habitat vs. non-habitat, to a greater appreciation of the composition and heterogeneity of land mosaics. Landscape-level inference will enable improved conservation outcomes by recognizing the influence of landscape properties on biota and devising strategies at this scale to complement patch-based management. We provide strong empirical evidence that biodiversity management in agricultural landscapes must focus on habitat extent. Complementary management of other landscape attributes, such as habitat aggregation and intensity of agricultural land-use, will also enhance the value of agricultural landscapes for woodland birds.

Responses of aquatic insect functional diversity to landscape changes in Atlantic forest

The Atlantic Forest domain, one of the 25 world's hotspots for biodiversity, has experienced dramatic changes in its landscape. While the loss of species diversity is well documented, functional diversity has not received the same amount of attention. In this study, we evaluated functional diversity of insects in streams utilizing three indices: functional diversity (FD), functional dispersion (FDis), and functional divergence (FDiv), seeking to understand the roles of three predictor sets in explaining functional patterns: (1) bioclimatic and landscape variables; (2) spatial variables; and (3) local environmental variables. We determined the amount of variation in different measures of functional diversity that was explained by each predictor set and their interplays using variation partitioning. Our study showed that variation in functional diversity is better explained by a set of variables linked to different scales dependent on spatial structures, indicating the importance of landscape and mainly environmental variables in the functional organization of aquatic insect communities, and that the relative importance of predictor sets depends on the indices considered. Variation in FD was better explained by the interplay among the three predictor sets and by local environmental variables, whereas variation in FDis was better explained by spatial variables and by the interplay between environmental and spatial variables. Variation in FDiv was not significantly explained by any predictors. Our study adds more evidence on the harmful effects caused by landscape changes on biodiversity in the Atlantic Forest, suggesting that these effects also influence the functional organization of stream insect communities. © 2013 The Author(s) Journal compilation © 2013 by The Association for Tropical Biology and Conservation.

Environmental suitability of a highly fragmented and heterogeneous landscape for forest bird species in south-eastern Brazil

Assessment of the suitability of anthropogenic landscapes for wildlife species is crucial for setting priorities for biodiversity conservation. This study aimed to analyse the environmental suitability of a highly fragmented region of the Brazilian Atlantic Forest, one of the world's 25 recognized biodiversity hotspots, for forest bird species. Eight forest bird species were selected for the analyses, based on point counts (n = 122) conducted in April-September 2006 and January-March 2009. Six additional variables (landscape diversity, distance from forest and streams, aspect, elevation and slope) were modelled in Maxent for (1) actual and (2) simulated land cover, based on the forest expansion required by existing Brazilian forest legislation. Models were evaluated by bootstrap or jackknife methods and their performance was assessed by AUC, omission error, binomial probability or p value. All predictive models were statistically significant, with high AUC values and low omission errors. A small proportion of the actual landscape (24.41 +/- 6.31%) was suitable for forest bird species. The simulated landscapes lead to an increase of c. 30% in total suitable areas. In average, models predicted a small increase (23.69 +/- 6.95%) in the area of suitable native forest for bird species. Being close to forest increased the environmental suitability of landscapes for all bird species; landscape diversity was also a significant factor for some species. In conclusion, this study demonstrates that species distribution modelling (SDM) successfully predicted bird distribution across a heterogeneous landscape at fine spatial resolution, as all models were biologically relevant and statistically significant. The use of landscape variables as predictors contributed significantly to the results, particularly for species distributions over small extents and at fine scales. This is the first study to evaluate the environmental suitability of the remaining Brazilian Atlantic Forest for bird species in an agricultural landscape, and provides important additional data for regional environmental planning.

Assessing landscape functional connectivity in a forest carnivore using path selection functions

Context Understanding connectivity patterns in relation to habitat fragmentation is essential to landscape management. However, connectivity is often judged from expert opinion or species occurrence patterns, with very few studies considering the actual movements of individuals. Path selection functions provide a promising tool to infer functional connectivity from animal movement data, but its practical application remains scanty. Objectives We aimed to describe functional connectivity patterns in a forest carnivore using path-level analysis, and to explore how connectivity is affected by land cover patterns and road networks. Methods We radiotracked 22 common genets in a mixed forest-agricultural landscape of southern Portugal. We developed path selection functions discriminating between observed and random paths in relation to landscape variables. These functions were used together with land cover information to map conductance surfaces. Results Genets moved preferentially within forest patches and close to riparian habitats. Functional connectivity declined with increasing road density, but increased with the proximity of culverts, viaducts and bridges. Functional connectivity was favoured by large forest patches, and by the presence of riparian areas providing corridors within open agricultural land. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the presence of crossing structures. Conclusions Genet movements were jointly affected by the spatial distribution of suitable habitats, and the presence of a road network dissecting such habitats and creating obstacles in areas otherwise permeable to animal movement. Overall, the study showed the value of path-level analysis to assess functional connectivity patterns in human-modified landscapes.

若尔盖湿地公路对两栖类的生态影响

本文考察了若尔盖高寒泥炭湿地公路对高原林蛙（Rana kukunoris）、倭蛙（Narorana pleskei）和岷山蟾蜍（Bufo minshanicus）的生态影响。分析了公路对两栖动物空间分布和栖息地利用的影响，并用IBM模型探讨其可能作用机制，考察了两栖动物公路死亡的季节差异及影响公路死亡空间分布的景观因素。最后通过对若尔盖高寒湿地两栖动物陆地核心栖息地的分析，为若尔盖路域栖息地的管理提供依据。 1. 对公路周边6个沼泽水凼群进行了调查，每个样地设置5条样线（距离公路10m、20m、50m、100m和150m）。调查表明，在繁殖季节(5月)，距离公路距离对高原林蛙和倭蛙的相对数量都有显著作用，其效应明显大于其他各项栖息地环境参数。公路导致高原林蛙和倭蛙在公路周边种群密度降低，其相对数量从距离公路100m处到公路边缘一直呈现逐渐降低的趋势。在繁殖季节，若尔盖高寒湿地的公路生态影响域大约在100-150m之间，这一距离远远大于森林栖息地中公路对两栖类的生态影响域（35-40 m）。 在繁殖后期（9月），对公路周边16个草地样点的样线调查表明，公路对周边高原林蛙和倭蛙密度分布并未造成显著影响。 2. 二次模型的拟合表明繁殖季节高原林蛙和倭蛙在公路周边的密度分布符合钟型曲线。前人对森林公路两侧两栖类分布的研究也显示了类似的规律。我们通过基于个体的模型，模拟在了公路边缘100单位距离内的栖息地空间，栖息地环境质量呈梯度变化，动物个体在其中通过随机运动寻找适宜的栖息地。拟合结果表明，动物个体仅仅依照简单的运动规则寻找适宜栖息地，这种活动就可以导致公路周边栖息地中的动物分布曲线出现3个局部峰。公路周边两栖动物的钟型分布曲线可能仅仅是个体寻找适宜栖息地过程中出现的临时性群体分布模式。 3. 在若尔盖高寒湿地，公路交通造成了大量两栖类死亡。但是公路两栖类动物死亡的季节分布很不均匀：5月、8月和9月死亡数量很高，而7月和10月死亡数量却很低。这种季节性差异和两栖类各个生活史阶段的迁移运动有密切的关系。利用景观参数的逻辑斯蒂回归模型显示，距离公路1000-2000m范围内的湿草地比例对三种两栖类公路死亡概率均有很强的贡献。湿草地这一栖息地类型分类中有大量的沼泽水体，是两栖类重要的繁殖点和取食点。两栖类公路死亡概率湿草地的关系从一个侧面表明，要维持一个区域较高的两栖类种群数量，需要1000-2000m半径范围内存在大面积的湿草地。 4. 高原林蛙和岷山蟾蜍不同性别和年龄个体分布点的水体距离存在显著差异。不同种类、年龄的两栖类分布点距离水体距离的差异可能是由于对水体的依赖性造成的。而相同种类、年龄段的个体中，高原林蛙雌性、岷山蟾蜍亚成体和雌性的体重与分布点距水体距离有显著负相关，这可能是因为体重更大的个体对水体的依赖性更弱。考虑到过大的陆地核心栖息地面积在实际保护工作中存在操作上的困难，因此我们认为可以以水体周边90%个体的分布区为低限确定3种两栖类的最小陆地核心栖息地。但是，在同样的水体距离-两栖类密度分布格局下，水体的面积和分形参数对最小陆地核心栖息地半径的确定有一定影响。 Ecological effects of alpine wetland road on Rana kukunoris, Narorana pleskei, Bufo minshanicus was studied in Zoige wetland. The effects of road on distribution of amphibians and its possible underline mechanism was discussed based on empirical data and computer simulation. Road killed amphibians was surveyed in different season and those landscape factor which could have impact on road killing distribution was analyses. Core terrestrial habitat of amphibians in Zoige wetland was discussed in the consideration of conservation management. 1. Six pool groups was investigated in breeding season (May) of R. kukunoris, N. pleskei. Five transects at distance of 10m, 20m, 50m, 100m and 150m from road edge was surveyed in each pool groups. There was a significant effects of distance from road edge on relative counts of R. kukunoris, N. pleskei, which is much important than effects of other environmental factors. Road caused the density of R. kukunoris, N. pleskei decreased from distance of 100m from road to 10m from road. Road ecological effect zone of alpine wetland for amphibians is about 100-150m. It is much wider than those of forest roads, which is about 35-40m. However, studies on 16 grassland near road showed no significant effect of road on amphibians after breeding season (Sep.). 2. Quadratic model fit indicated that the distribution of R. kukunoris and N. Pleskei followed a hump like curve. Previous studies on forest road showed similar results. A 100×100 habitat with gradual environment besides road was simulated with a individual-based model, and animal seek for suitable habitat with stochastic locomotion in it. Simulation results indicated that 3 density peak of animal distribution can emergent followed a simply rules. The hump like density cure could be a temporal swarm pattern during the process of individual seeking for habitat. 3. Road traffic caused mass death of amphibians in Zoige wetland. There was much road killed amphibians in May, Aug and Sep than those in July and Oct. The fluctuation of road kill could be related with migration of amphibians between seasons. Logistic regression of landscape variables indicated that wet grassland in 1000-2000m is essential to predict the probability of road kill. Wet grassland is an important breeding and forage habitat for amphibians. It also indicated that mass wet grassland in 1000-2000m is essential for maintain a big amphibian population. 4. There was significant differences among distance from aquatic site of subadults, female and males of R. kukunoris and B. Minshanicus. Possibly, it was because of their dependence on water. There was a significant negative relationship between distance from aquatic site and individuals body mass. Estimates of core habitat that are too large may make it difficult to establish protective regulations. The smallest suitable terrestrial core habitats were defined as the terrestrial habitats used during migration to and from the wetlands, and for foraging by 90% of any life stage (adults, and subadults) in a season. However, even with the same amphibian distribution pattern along the distance from aquatic sites, the radii of smallest suitable terrestrial core habitats will be varied with the fractal parameters of aquatic site.

Models to predict the distribution and abundance of breeding ducks in Canada

Detailed knowledge of waterfowl abundance and distribution across Canada is lacking, which limits our ability to effectively conserve and manage their populations. We used 15 years of data from an aerial transect survey to model the abundance of 17 species or species groups of ducks within southern and boreal Canada. We included 78 climatic, hydrological, and landscape variables in Boosted Regression Tree models, allowing flexible response curves and multiway interactions among variables. We assessed predictive performance of the models using four metrics and calculated uncertainty as the coefficient of variation of predictions across 20 replicate models. Maps of predicted relative abundance were generated from resulting models, and they largely match spatial patterns evident in the transect data. We observed two main distribution patterns: a concentrated prairie-parkland distribution and a more dispersed pan-Canadian distribution. These patterns were congruent with the relative importance of predictor variables and model evaluation statistics among the two groups of distributions. Most species had a hydrological variable as the most important predictor, although the specific hydrological variable differed somewhat among species. In some cases, important variables had clear ecological interpretations, but in some instances, e.g., topographic roughness, they may simply reflect chance correlations between species distributions and environmental variables identified by the model-building process. Given the performance of our models, we suggest that the resulting prediction maps can be used in future research and to guide conservation activities, particularly within the bounds of the survey area.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.

Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach

1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low   abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal.
2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat.
3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike’s information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability.
4. Negative associations between rufous bristlebird presence and  increasing elevation, 'distance to cree', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation.
5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant  independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥0·5 level ) was 16%.
6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multi-scale  approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.

Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia

In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence greater-or-equal, slanted0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool.

Predictive distribution models and their application in wildlife conservation

Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.

Assessment of Cerdocyon thous distribution in an agricultural mosaic, southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Diversidade das comunidades de peixes de riachos em função da estrutura da paisagem em microbacias da bacia do rio Corumbataí, SP

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multiple Determinants of Anuran Richness and Occurrence in an Agricultural Region in South-Eastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)