Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

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.     

Assessing extinction-risk of endangered plants using species distribution models: a case study of habitat depletion caused by the spread of greenhouses

The species distribution models (SDMs) are useful tools for investigating rare and endangered species as well as the environmental variables affecting them. In this paper, we propose the application of SDMs to assess the extinction-risk of plant species in relation to the spread of greenhouses in a Mediterranean landscape, where habitat depletion is one of the main causes of biodiversity loss. For this purpose, presence records of the model species (Linaria nigricans, a endemic and threatened species) and the greenhouses, a dataset of environmental variables, and different only presence-based modelling algorithms (Bioclim, Domain, GARP, MaxEnt and ENFA) were used to build SDMs for L. nigricans as well as for greenhouses. To evaluate the models a modified approach of the area-under-curve ROC was applied. Combining the most accurate models, we generated an extinction-risk model of L. nigricans populations, which enabled us to assess the sustainability of the most threatened populations. Our results show that is possible to model greenhouses spreading as a “biological invasion”. The procedure explained and used in this work is quite novel, and offers an objective spatial criterion intended for the management of natural resources and for the conservation of the biodiversity in areas threatened by habitat depletion processes as particular as greenhouses expansion.     

Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States

Many critical ecological issues require the analysis of large spatial point data sets – for example, modelling species distributions, abundance and spread from survey data. But modelling spatial relationships, especially in large point data sets, presents major computational challenges. We use a novel Bayesian hierarchical statistical approach, 'spatial predictive process' modelling, to predict the distribution of a major invasive plant species, Celastrus orbiculatus , in the northeastern USA. The model runs orders of magnitude faster than traditional geostatistical models on a large data set of c . 4000 points, and performs better than generalized linear models, generalized additive models and geographically weighted regression in cross-validation. We also use this approach to model simultaneously the distributions of a set of four major invasive species in a spatially explicit multivariate model. This multispecies analysis demonstrates that some pairs of species exhibit negative residual spatial covariation, suggesting potential competitive interaction or divergent responses to unmeasured factors.     

Using range filling rather than prevalence of invasive plant species for management prioritisation: the case of Spathodea campanulata in the Society Islands (South Pacific)

Biological invasion science lacks standardised measures of invasion success that would provide effective prioritisation of invasive species and invaded areas management. Prevalence (area of occupancy) of invasive species is often used as proxy of their success but this metric ignores the extent to which a species fills its potential distribution. This study aims to estimate the performance of invasive tree species by computing the ratio between the compressed canopy area (CCA), assessed through remote sensing, and their potential distribution, estimated using invasive species distribution modelling. This index of ‘range filling’ (RF) has applicability to a broad set of invasive plant species in any biome. A case study is provided using the invasive African tulip tree Spathodea campanulata (Bignoniaceae) on three small tropical oceanic islands (South Pacific) exhibiting different invasion levels to test for differences between CCA and RF. The results show that the RF of Spathodea campanulata varied within islands depending on elevation but not proportionally to the CCA of the species. Another key result was that the RF of the species and its CCA provided different between-island perspectives on the invasions and lead to distinct ranking among islands to prioritise for management. Therefore, managers should disregard species’ prevalence as a measure of success and rather weight it with potential distribution to quantify how an invader is performing in a given environment.     

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.     

Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA

To advance the development of conservation planning for rare species with small geographic ranges, we determined habitat associations of Siskiyou Mountains salamanders (Plethodon stormi) and developed habitat suitability models at fine (10 ha), medium (40 ha), and broad (202 ha) spatial scales using available Geographic Information Systems data and logistic regression analysis with an information theoretic approach. Across spatial scales, there was very little support for models with structural habitat features, such as tree canopy cover and conifer diameter. Model-averaged 95% confidence intervals for regression coefficients and associated odds ratios indicated that the occurrence of Siskiyou Mountains salamanders was positively associated with rocky soils and Pacific madrone (Abutus menziesii) and negatively associated with elevation and white fir (Abies concolor); these associations were consistent across 3 spatial scales. The occurrence of this species also was positively associated with hardwood density at the medium spatial scale. Odds ratios projected that a 10% decrease in white fir abundance would increase the odds of salamander occurrence 3.02–4.47 times, depending on spatial scale. We selected the model with rocky soils, white fir, and Oregon white oak (Quercus garryana) as the best model across 3 spatial scales and created habitat suitability maps for Siskiyou Mountains salamanders by projecting habitat suitability scores across the landscape. Our habitat suitability models and maps are applicable to selection of priority conservation areas for Siskiyou Mountains salamanders, and our approach can be easily adapted to conservation of other rare species in any geographical location.     

气候变化对物种分布影响模拟中的不确定性组分分割与制图——以油松为例

物种分布模型是预测评估气候变化对物种分布影响的主要工具。为了降低物种分布模型在预测过程中的不确定性,近期有学者提出了采用组合预测的新方法,即采用多套建模数据、模型技术,模型参数,以及环境情景数据对物种分布进行预测,构成物种分布预测集合。但是,组合预测中各组分对变异的贡献还知之甚少,因此有必要把变异组分来源进行分割,以更有效地利用组合预测方法来降低模型预测中的不确定性。以油松为例,采用8个生态位模型,9套模型训练数据,3个GCM模型和一个SRES(A2)排放情景,模型分析了油松当前(1961-1990年)和未来气候条件下3个时间段(2010-2039年,2040-2069年,2070-2099年)的潜在分布。共计得到当前分布预测数据72套,未来每个时间段分布数据216套。采用开发的ClimateChina软件进行当前和未来气候数据的降尺度处理。采用Kappa、真实技巧统计方法(TSS)和接收机工作特征曲线下的面积(AUC)对模型预测能力进行评估。结果表明,随机森林(RF)、广义线性模型(GLM),广义加法模型(GAM)、多元自适应样条函数(MARS)以及助推法(GBM)预测效果较好,几乎不受建模数据之间差异的影响。混合判别分析模型(MDA)对建模数据之间的差异非常敏感,甚至出现建模失败现象。采用三因素方差分析方法对组合预测中的不确定性来源进行变异分割,结果表明,模型之间的差异对模拟预测结果不确定性的贡献最大且所占比例极高,而建模数据之间的差异贡献最小,GCM贡献居中。研究将有助于加深对物种分布模拟预测中不确定性的认识。     

Levels of endemism are not necessarily biased by the co-presence of species with different range sizes: a case study of Vilenkin and Chikatunov's models

Aim  To illustrate problems in the methods proposed by B. Vilenkin and V. Chikatunov to study levels of endemism and species–area relationships.
Location  The study used data on the distribution of tenebrionid beetles (Coleoptera, Tenebrionidae) on the Aegean Islands (Greece).
Methods  A total of 32 islands and 170 taxa (species and subspecies) were included in this study. Levels of endemism were evaluated both as the proportion of endemic taxa, and according to the methods proposed by Vilenkin and Chikatunov, which are based on the number of non-endemic taxa and various relationships with area. A model of the species–area relationship proposed by these authors was also analysed.
Results  The number of endemic taxa was positively correlated with the number of taxa with different distribution types, but this positive correlation did not influence the estimation of the level of endemism. In fact, the commonly used estimate of endemicity as a percentage was strongly correlated with the endemism values calculated according to the method of Vilenkin and Chikatunov. The usual power function fitted the species–area relationship as well as the most complicated method of Vilenkin and Chikatunov.
Main conclusions  As hypothesized by Vilenkin and Chikatunov, the number of endemic taxa was influenced both by the number of taxa of other biogeographical ranks, and by an island's area. However, explanations for the positive relationship between the number of endemic taxa and taxa of different biogeographical ranks are equivocal. Importantly, this relationship did not necessarily influence the level of endemism, which could be expressed adequately by percentages. The method proposed by Vilenkin and Chikatunov to estimate the species–area relationship cannot be clearly justified on theoretical grounds and is of questionable practical utility.     

The importance of setting the right genetic distance threshold for identification of clones using amplified fragment length polymorphism: a case study with five species in the tropical plant genus Piper

Amplified fragment length polymorphism (AFLP) has been widely used for clone identification, but numerous studies have shown that clonemates do not always present identical AFLP fingerprints. Pairwise AFLP distances that distinguish known clones from nonclones have been used to identify a threshold genetic dissimilarity distance below which samples are considered to represent a single clone. Most studies to date have reported threshold values between 2% and 4%. Here, I determine the consistency of the clonal threshold across five species in the tropical plant genus Piper, and evaluate the sensitivity of genetic diversity indices and estimates of frequency of clonal reproduction to the threshold value selected. I sampled multiple ramets per individual from widely distributed plants for each of the five Piper species to set a threshold at the point where the error rate of clonal assignments was lowest. I then sampled all individuals of each shade‐tolerant species in a 1‐ha plot, and of each light‐demanding species in 25 × 35‐m plot, to estimate the frequency of asexual recruitment in natural populations using a series of different thresholds including the threshold set with the preliminary sampling. Clonal threshold values for the different species ranged from 0% to 5% AFLP genetic dissimilarity distance. To determine the sensitivity of estimates of clonal reproduction, I calculated several clonal diversity indexes for the natural populations of each of the five species guided by the range in clonal threshold values observed across the five Piper species. I show that small changes in the value of the clonal threshold can lead to very different conclusions regarding the level of clonal reproduction in natural populations.