Limited transferability of stream‐fish distribution models among river catchments: reasons and implications

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Is current climatic equilibrium a guarantee for the transferability of distribution model predictions? A case study of the spotted hyena

Aim The transferability of species distribution models requires that species show climatic equilibrium throughout their entire distribution area. We test this assumption for the case of the spotted hyena, Crocuta crocuta, a large carnivore that has shifted its distribution over the last 100,000 years from a widespread Eurasian and African range to its current geographical distribution, restricted to the Sub‐Saharan areas of the African continent. Location Western Eurasia and Africa. Methods The current realized distribution of C. crocuta was estimated using presences and reliable absences as well as climatic, land‐cover and anthropic variables as predictors. The potential distribution was estimated using presences and a set of pseudo‐absences selected from localities outside climatically suitable localities, with only climatic variables serving as predictors. The current potential distribution was transferred to the Last Interglacial period (126,000 yr bp ) using the palaeoclimatic data yielded by the GENESIS 2 general circulation model, and validated with European fossil data. Generalized linear models were used on all occasions. Results Climatic variables are able to predict the current distribution of the species with high accuracy. The geographical projection of this model indicates that the species is distributed over almost all of its potential suitable area, which allows us to suppose that the current distribution of this species is in climatic equilibrium. However, the time transference of model predictions for the western Eurasian region reveals almost no suitable conditions for hyenas, despite the widespread presence of C. crocuta fossil remains on this continent during the Last Interglacial period. Main conclusions Our results indicate that, even when model results suggest a climatic equilibrium for a species distribution, the time transferability of such models does not necessarily provide realistic results. This occurs because the current geographical range does not allow estimations of all of the environmental requirements of a species. Therefore, any model trained with current data risks underestimating the potential suitable environmental and geographical range for species in a new area or time period.     

Modeling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability

Predictions of occurrence of two amphibian species, the common toad and the alpine newt, were made using information on land use surrounding breeding ponds. A geographical information system (GIS) was used to compile a landuse map, from which permeability estimates (friction) were derived. Potential migration zones based on friction and maximum migration distance were then modeled. Contacts between several migration zones suggest the potential for migration between ponds by adult individuals. The ability of the migration zones to enhance predictions of species occurrence was tested using generalized additive models (GAMs), and several landscape variables were selected as determinants of amphibian presence. The area of a migration zone and the number of ponds within that zone were positively related to both toad and newt presence, suggesting the importance of buffer habitats around each pond in amphibian conservation. Toad presence declined with cultivated field area and newt presence declined with vineyard area, suggesting the negative effect of agricultural activity on amphibians. The friction-based modeling approach improved the prediction of toad presence when compared to a more classical analysis of habitat composition within a circular zone centered on the focal pond. Prediction of newt presence was, however, less accurate than prediction of toad presence. Despite its exploratory nature and the subjectivity of permeability estimates, this study shows the usefulness of GIS in the functional analysis of a landscape, with potential applications in biological conservation. It also highlights the need for improving our knowledge of habitat use by animals.     

Developing an approach to defining the potential distributions of invasive plant species: a case study of Hakea species in South Africa

Aim  Models of the potential distributions of invading species have to deal with a number of issues. The key one is the high likelihood that the absence of an invading species in an area is a false absence because it may not have invaded that area yet, or that it may not have been detected. This paper develops an approach for screening pseudo-absences in a way that is logical and defensible.
Innovation  The step-wise approach involves: (1) screening environmental variables to identify those most likely to indicate conditions where the species cannot invade; (2) identifying and selecting the most likely limiting variables; (3) using these to define the limits of its invasion potential; and (4) selecting points outside these limits as true absence records for input into species distribution models.
This approach was adopted and used for the study of three prominent Hakea species in South Africa. Models with and without the false absence records were compared. Two rainfall variables and the mean minimum temperature of the coldest month were the strongest predictors of potential distributions. Models which excluded false absences predicted that more of the potential distribution would have a high invasion potential than those which included them.
Main conclusions  The approach of applying a priori knowledge can be useful in refining the potential distribution of a species by excluding pseudo-absence records which are likely to be due to the species not having invaded an area yet or being undetected. The differences between the potential distributions predicted by the different models convey more information than making a single prediction, albeit a consensus model. The robustness of this approach depends strongly on an adequate knowledge of the ecology, invasion history and current distribution of that species.     

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.     

Using species distribution models to locate animal aggregations: a case study with Hippodamia undecimnotata (Schneider) overwintering aggregation sites

1. The protection of animals' aggregation sites is increasingly seen as a key conservation strategy. However, to efficiently protect aggregation sites, they need to be accurately located. Species distribution models (SDMs) are an important tool in biological conservation to predict spatial distribution of species and they are used here to predict the distribution of the aggregation sites of a ladybird (Coleoptera: Coccinellidae) species. 2. Hippodamia undecimnotata forms spectacular overwintering aggregations at the same locations every year across southern and eastern Europe. In this study, an SDM was developed and its performance tested for H. undecimnotata aggregations in southwest France. Moreover, the study looked at how environmental variables correlate with ladybirds' abundance in the aggregation sites. 3. The occurrence of aggregations was best described by one model including isolated prominent object, pesticide risk, altitude and vegetation coverage. Furthermore, ladybird abundance at the aggregation sites is positively correlated with altitude. The SDM occurrence model performance was found to be high (area under the curve = 0.92 and true skill statistic = 0.78). 4. It is suggested that H. undecimnotata may be an umbrella species, because some aggregation sites were also used by other ladybird species. Therefore, the protection of the aggregation sites of this species may benefit several other species. 5. This study provides nature conservation stakeholders with a tool to locate overwintering aggregations, a first step towards the protection of these sites.     

Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant

Aim We demonstrate how to integrate two widely used tools for modelling the spread of invasive plants, and compare the performance of the combined model with that of its individual components using the recent range dynamics of the invasive annual weed Ambrosia artemisiifolia L. Location Austria. Methods Species distribution models, which deliver habitat‐based information on potential distributions, and interacting particle systems, which simulate spatio‐temporal range dynamics as dependent on neighbourhood configurations, were combined into a common framework. We then used the combined model to simulate the invasion of A. artemisiifolia in Austria between 1990 and 2005. For comparison, simulations were also performed with models that accounted only for habitat suitability or neighbourhood configurations. The fit of the three models to the data was assessed by likelihood ratio tests, and simulated invasion patterns were evaluated against observed ones in terms of predictive discrimination ability (area under the receiver operating characteristic curve, AUC) and spatial autocorrelation (Moran’s I). Results The combined model fitted the data significantly better than the single‐component alternatives. Simulations relying solely on parameterized spread kernels performed worst in terms of both AUC and spatial pattern formation. Simulations based only on habitat information correctly predicted infestation of susceptible areas but reproduced the autocorrelated patterns of A. artemisiifolia expansion less adequately than did the integrated model. Main conclusions Our integrated modelling approach offers a flexible tool for forecasts of spatio‐temporal invasion patterns from landscape to regional scales. As a further advantage, scenarios of environmental change can be incorporated consistently by appropriately updating habitat suitability layers. Given the susceptibility of many alien plants, including A. artemisiifolia, to both land use and climate changes, taking such scenarios into account will increasingly become relevant for the design of proactive management strategies.     

Using broad-scale, community survey data to compare species conservation strategies across regions: A case study of the Koala in a set of adjacent 'catchments'

Summary   Managers of wildlife populations with a wide geographical range are understandably interested in the question of whether they can manage a broader population with a single conservation strategy (e.g. covering a set of adjacent management regions, referred to as 'catchments' in Australia) or whether separate strategies are required for individual catchments. We addressed this question using data from a statewide, community wildlife survey to quantify Koala ( Phascolarctos cinereus ) habitat relationships in the catchments of four adjacent Catchment Management Authorities or CMA (>10 000 km²) of New South Wales, Australia and then tested whether these habitat relationships were similar across catchments. Although the results were constrained by the coarse resolution of the community survey and environmental data, we were able to model broad-scale patterns of habitat use. Model explanatory power and cross-regional predictability was low, but consistent with Koala ecology. Two environmental variables emerged as having a strong relationship with Koala presence – mean elevation and percentage of fertile soils – the importance of which varied among catchments depending on land-use patterns. The results highlight the need for local wildlife management plans, not a single plan covering multiple catchments.     

A critical assessment of two species distribution models: a case study of the vervet monkey (Cercopithecus aethiops)

Aim Species distribution models are invaluable tools in biogeographical, ecological and applied biological research, but specific concerns have been raised in relation to different modelling techniques in terms of their validity. Here we compare two fundamentally different approaches to species distribution modelling, one based on simple occurrence data where the lack of an ecological framework has been criticized, and the other firmly based in socio‐ecological theory but requiring highly detailed behavioural information that is often limited in availability. Location (Sub‐Saharan) Africa. Methods We used two distinct techniques to predict the realized distribution of a model species, the vervet monkey (Cercopithecus aethiops Linnaeus, 1758). A maximum entropy model was produced taking 13 environmental variables and presence‐only data from 174 sites throughout Africa as input, with an additional 58 sites retained to test the model. A time‐budget model considering the same environmental variables was constructed from detailed behavioural data on 20 groups representing 14 populations, with presence‐only data from the remaining 218 sites reserved to test model predictions on vervet monkey occurrence. Both models were further validated against a reference species distribution map as drawn up by the African Mammals Databank. Results Both models performed well, with the time budget and maximum entropy algorithms correctly predicting vervet monkey presence at 78.4% and 91.4% of their respective test sites. Similarly, the time‐budget model correctly predicted presence and absence at 87.4% of map pixels against the reference distribution map, and the maximum entropy model achieved a success rate of 81.8%. Finally, there was a high level of agreement (81.6%) between the presence–absence maps produced by the two models, and the environmental variables identified as most strongly driving vervet monkey distribution were the same in both models. Main conclusions The time‐budget and maximum entropy models produced accurate and remarkably similar species distribution maps, despite fundamental differences in their conceptual and methodological approaches. Such strong convergence not only provides support for the credibility of current results, but also relieves concerns about the validity of the two modelling approaches.     

Measuring the effectiveness of regional conservation assessments at representing biodiversity surrogates at a local scale: A case study in Réunion Island (Indian Ocean)

In a context of scarce financial and human resources, the allocation of conservation efforts needs to be optimized. Our analysis attempts to draw conclusions on the integration of regional and local conservation assessments, specifically, with regard to the acquisition of fine‐scale data to complement the regional assessment. This study undertaken in Réunion Island (Indian Ocean) assessed how biodiversity surrogates targeted at a regional scale represented other biodiversity surrogates at a local scale. Biodiversity surrogates at both scales consisted of species, habitats and processes. Habitats and processes at regional scale were defined using a coarser scale of thematic resolution than at local scale. The surrogacy was tested in terms of incidental representation of local‐scale features in the regional assessments, and correlation of irreplaceability values between scales. Near‐minimum sets and irreplaceability values were generated using MARXAN software. Our results revealed that conservation targets for processes at local scale were never met incidentally, while threatened species and fragmented habitats were also usually under‐represented. More specifically, requiring only 12% of the local planning domain, the application of species as surrogates at regional scale was the least effective option at representing biodiversity features at local scale. In contrast, habitats at a coarse scale of thematic resolution achieved a significant proportion of conservation targets incidentally (67%) and their irreplaceability values were well correlated with the irreplaceability values of surrogates at local scale. The results highlighted that all three types of biodiversity surrogates are complementary for assessing overall biodiversity. Because of the cost of data acquisition, we recommended that the most efficient strategy to develop nested regional/local conservation plans is to apply habitats and processes at a coarse scale of thematic resolution at regional scale, and threatened species and degraded habitats at local scale, with their fine‐scale mapping limited to highly transformed areas.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Spatial sampling bias and model complexity in stream‐based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin,USA

Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AIC_C‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AIC_C‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.