Reference state and benchmark concepts for better biodiversity conservation in contemporary ecosystems.

Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterize currently recognized historical reference states and describe them as Pre-Human, Indigenous Cultural, Pre-Intensification and Hybrid-Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values relative to other locations within the same ecosystem, regardless of the disturbance history. We acknowledge that past processes play an essential role in driving contemporary patterns of diversity. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal-to maximize biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g. native species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This 'road map of reference states' offers perspective needed to define and assess the status and trends in biodiversity and habitats. We demonstrate the contemporary reference state concept with an example from south-eastern Australia. Our framework provides a tractable way for policy-makers and practitioners to navigate biodiversity assessments to maximize conservation and restoration outcomes in contemporary ecosystems.

Computationally efficient joint species distribution modeling of big spatial data.

The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

Identifying wildlife corridors for the restoration of regional habitat connectivity: A multispecies approach and comparison of resistance surfaces.

Many large-scale connectivity initiatives have been proposed around the world with the aim of maintaining or restoring connectivity to offset the impacts on biodiversity of habitat loss and fragmentation. Frequently, these are based on the requirements of a single focal species of concern, but there is growing attention to identifying connectivity requirements for multi-species assemblages. A number of methods for modelling connectivity have been developed; likewise, different approaches have been used to construct resistance surfaces, the basic input data for connectivity analyses. In this study we modelled connectivity for a multi-species group of vertebrates representative of heavily fragmented forests in north-central Victoria, Australia. For each species, we used least-cost modelling and compared two alternate resistance surfaces, based on species distribution models and on expert opinion, respectively. We integrated the connectivity results across individual species to obtain a multi-species connectivity map for the region. A resistance surface based on expert assessment of the relative use of land-cover classes by the target species was more informative than one based on species distribution models. The former resulted in pathways more strongly aligned with existing patches and strips of native vegetation. In this region, pathways aligned with streams and their associated riparian vegetation have relatively high ecological potential and feasibility to contribute to regional connectivity for the assemblage of forest vertebrates.

On the selection of thresholds for predicting species occurrence with presence-only data.

Presence-only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and maxF pb with four presence-only datasets with different ratios of the number of known presences to the number of random points (KP-RP ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF pb varied as the KP-RP ratio of the threshold selection datasets changed. Datasets with the KP-RP ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF pb is affected by the KP-RP ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach.

Species- and sex-specific connectivity effects of habitat fragmentation in a suite of woodland birds.

Loss of functional connectivity following habitat loss and fragmentation could drive species declines. A comprehensive understanding of fragmentation effects on functional connectivity of an ecological assemblage requires investigation of multiple species with different mobilities, at different spatial scales, for each sex, and in different landscapes. Based on published data on mobility and ecological responses to fragmentation of 10 woodland-dependent birds, and using simulation studies, we predicted that (1) fragmentation would impede dispersal and gene flow of eight "decliners" (species that disappear from suitable patches when landscape-level tree cover falls below species-specific thresholds), but not of two "tolerant" species (whose occurrence in suitable habitat patches is independent of landscape tree cover); and that fragmentation effects would be stronger (2) in the least mobile species, (3) in the more philopatric sex, and (4) in the more fragmented region. We tested these predictions by evaluating spatially explicit isolation-by-landscape-resistance models of gene flow in fragmented landscapes across a 50 x 170 km study area in central Victoria, Australia, using individual and population genetic distances. To account for sex-biased dispersal and potential scale- and configuration-specific effects, we fitted models specific to sex and geographic zones. As predicted, four of the least mobile decliners showed evidence of reduced genetic connectivity. The responses were strongly sex specific, but in opposite directions in the two most sedentary species. Both tolerant species and (unexpectedly) four of the more mobile decliners showed no reduction in gene flow. This is unlikely to be due to time lags because more mobile species develop genetic signatures of fragmentation faster than do less mobile ones. Weaker genetic effects were observed in the geographic zone with more aggregated vegetation, consistent with gene flow being unimpeded by landscape structure. Our results indicate that for all but the most sedentary species in our system, the movement of the more dispersive sex (females in most cases) maintains overall genetic connectivity across fragmented landscapes in the study area, despite some small-scale effects on the more philopatric sex for some species. Nevertheless, to improve population viability for the less mobile bird species, structural landscape connectivity must be increased.

Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds.

Inference concerning the impact of habitat fragmentation on dispersal and gene flow is a key theme in landscape genetics. Recently, the ability of established approaches to identify reliably the differential effects of landscape structure (e.g. land-cover composition, remnant vegetation configuration and extent) on the mobility of organisms has been questioned. More explicit methods of predicting and testing for such effects must move beyond post hoc explanations for single landscapes and species. Here, we document a process for making a priori predictions, using existing spatial and ecological data and expert opinion, of the effects of landscape structure on genetic structure of multiple species across replicated landscape blocks. We compare the results of two common methods for estimating the influence of landscape structure on effective distance: least-cost path analysis and isolation-by-resistance. We present a series of alternative models of genetic connectivity in the study area, represented by different landscape resistance surfaces for calculating effective distance, and identify appropriate null models. The process is applied to ten species of sympatric woodland-dependant birds. For each species, we rank a priori the expectation of fit of genetic response to the models according to the expected response of birds to loss of structural connectivity and landscape-scale tree-cover. These rankings (our hypotheses) are presented for testing with empirical genetic data in a subsequent contribution. We propose that this replicated landscape, multi-species approach offers a robust method for identifying the likely effects of landscape fragmentation on dispersal.

Raising the bar for systematic conservation planning.

Systematic conservation planning (SCP) represents a significant step toward cost-effective, transparent allocation of resources for biodiversity conservation. However, research demonstrates important consequences of uncertainties in SCP and of basing methods on simplified circumstances involving few real-world complexities. Current research often relies on single case studies with unknown forms and amounts of uncertainty as well as low statistical power for generalizing results. Consequently, conservation managers have little evidence for the true performance of conservation planning methods in their own complex, uncertain applications. To build effective and reliable methods in SCP, there is a need for more challenging and integrated testing of their robustness to uncertainty and complexity, and much greater emphasis on generalization to real-world situations.