An integrated risk assessment for climate change: analysing the vulnerability of sharks and rays on Australia's Great Barrier Reef

An Integrated Risk Assessment for Climate Change (IRACC) is developed and applied to assess the vulnerability of sharks and rays on Australia's Great Barrier Reef (GBR) to climate change. The IRACC merges a traditional climate change vulnerability framework with approaches from fisheries ecological risk assessments. This semi‐quantitative assessment accommodates uncertainty and can be applied at different spatial and temporal scales to identify exposure factors, at‐risk species and their key biological and ecological attributes, critical habitats a`nd ecological processes, and major knowledge gaps. Consequently, the IRACC can provide a foundation upon which to develop climate change response strategies. Here, we describe the assessment process, demonstrate its application to GBR shark and ray species, and explore the issues affecting their vulnerability to climate change. The assessment indicates that for the GBR, freshwater/estuarine and reef associated sharks and rays are most vulnerable to climate change, and that vulnerability is driven by case‐specific interactions of multiple factors and species attributes. Changes in temperature, freshwater input and ocean circulation will have the most widespread effects on these species. Although relatively few GBR sharks and rays were assessed as highly vulnerable, their vulnerability increases when synergies with other factors are considered. This is especially true for freshwater/estuarine and coastal/inshore sharks and rays. Reducing the impacts of climate change on the GBR's sharks and rays requires a range of approaches including mitigating climate change and addressing habitat degradation and sustainability issues. Species‐specific conservation actions may be required for higher risk species (e.g. the freshwater whipray, porcupine ray, speartooth shark and sawfishes) including reducing mortality, preserving coastal catchments and estuarine habitats, and addressing fisheries sustainability. The assessment identified many knowledge gaps concerning GBR habitats and processes, and highlights the need for improved understanding of the biology and ecology of the sharks and rays of the GBR.     

Climate‐niche factor analysis: a spatial approach to quantifying species vulnerability to climate change

Climate change vulnerability assessments are an important tool for understanding the threat that climate change poses to species and populations, but do not generally yield insight into the spatial variation in vulnerability throughout a species’ habitat. We demonstrate how to adapt the method of ecological‐niche factor analysis (ENFA) to objectively quantify aspects of species sensitivity to climate change. We then expand ENFA to quantify aspects of exposure and vulnerability to climate change as well, using future projections of global climate models. This approach provides spatially‐explicit insight into geographic patterns of vulnerability, relies only on readily‐available spatial data, is suitable for a wide range of species and habitats, and invites comparison between different species. We apply our methods to a case study of two species of montane mammals, the American pika Ochotona princeps and the yellow‐bellied marmot Marmota flaviventris.     

Using fuzzy logic to determine the vulnerability of marine species to climate change

Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species‐specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the ‘business‐as‐usual' greenhouse gas emission scenario (Representative Concentration Pathway 8.5). We identified 157 species to be highly vulnerable while 294 species are identified as being at high risk of impacts. Species that are most vulnerable tend to be large‐bodied endemic species. This study suggests that the fuzzy logic framework can help estimate climate vulnerabilities and risks of exploited marine species using publicly and readily available information.     

The impact and implications of climate change for bats

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气候变化背景下野生动物脆弱性评估方法研究进展

脆弱性评估是研究气候变化影响野生动物的重要内容,识别野生动物脆弱性,是适应和减缓气候变化影响的关键和基础。开展气候变化背景下野生动物的脆弱性评估工作,目的是为了确定易受气候变化影响的物种和明确导致物种脆弱性的因素,其评估结果有助于人类认识气候变化对野生动物的影响,为野生动物适应气候变化保护对策的制定提供科学依据。对野生动物而言(物种),脆弱性是物种受气候变化影响的程度,包括暴露度、敏感性和适应能力三大要素。其中,暴露度是由气候变化引起的外在因素,如温度、降雨量、极值天气等;敏感性是受物种自身因素影响,如种间关系、耐受性等;适应能力是物种通过自身调整来减小气候变化带来的影响,如迁移或扩散到适宜生境的能力、塑性反应和进化反应等。对近期有关气候变化背景下野生动物脆弱性评估方法予以综述,比较每种评估方法所选取指标的差异,总结在脆弱性评估中遇到的不确定性指标的处理方法,以及脆弱性评估结果在野生动物适应气候变化对策中的应用。通过总结野生动物脆弱性评估方法,以期为气候变化背景下评估我国野生动物资源的脆弱性提供参考方法。     

Sensitivity analysis of the tree distribution model Phenofit to climatic input characteristics: implications for climate impact assessment

Species distributions are already affected by climate change. Forecasting their long‐term evolution requires models with thoroughly assessed validation. Our aim here is to demonstrate that the sensitivity of such models to climate input characteristics may complicate their validation and introduce uncertainties in their predictions. In this study, we conducted a sensitivity analysis of a process‐based tree distribution model Phenofit to climate input characteristics. This analysis was conducted for two North American trees which differ greatly in their distribution and eight different types of climate input for the historic period which differ in their spatial (local or gridded data) and temporal (daily vs. monthly) resolution as well as their type (locally recorded, extrapolated or simulated by General Circulation Models). We show that the climate data resolution (spatial and temporal) and their type, highly affect the model predictions. The sensitivity analysis also revealed, the importance, for global climate change impact assessment, of (i) the daily variability of temperatures in modeling the biological processes shaping species distribution, (ii) climate data at high latitudes and elevations and (iii) climate data with high spatial resolution.     

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.     

Trait‐based climate vulnerability assessments in data‐rich systems: An application to eastern Bering Sea fish and invertebrate stocks

Trait‐based climate vulnerability assessments based on expert evaluation have emerged as a rapid tool to assess biological vulnerability when detailed correlative or mechanistic studies are not feasible. Trait‐based assessments typically view vulnerability as a combination of sensitivity and exposure to climate change. However, in some locations, a substantial amount of information may exist on system productivity and environmental conditions (both current and projected), with potential disparities in the information available for data‐rich and data‐poor stocks. Incorporating this level of detailed information poses challenges when conducting, and communicating uncertainty from, rapid vulnerability assessments. We applied a trait‐based vulnerability assessment to 36 fish and invertebrate stocks in the eastern Bering Sea (EBS), a data‐rich ecosystem. In recent years, the living marine resources of the EBS and Aleutian Islands have supported fisheries worth more than US $1 billion of annual ex‐vessel value. Our vulnerability assessment uses projections (to 2039) from three downscaled climate models, and graphically characterizes the variation in climate projections between climate models and between seasons. Bootstrapping was used to characterize uncertainty in specific biological traits and environmental variables, and in the scores for sensitivity, exposure, and vulnerability. The sensitivity of EBS stocks to climate change ranged from “low” to “high,” but vulnerability ranged between “low” and “moderate” due to limited exposure to climate change. Comparison with more detailed studies reveals that water temperature is an important variable for projecting climate impacts on stocks such as walleye pollock (Gadus chalcogrammus), and sensitivity analyses revealed that modifying the rule for determining vulnerability increased the vulnerability scores. This study demonstrates the importance of considering several uncertainties (e.g., climate projections, biological, and model structure) when conducting climate vulnerability assessments, and can be extended in future research to consider the vulnerability of user groups dependent on these stocks.     

Development and assessment of indices to determine stream fish vulnerability to climate change and habitat alteration

Understanding the future impacts of climate and land use change are critical for long-term biodiversity conservation. We developed and compared two indices to assess the vulnerability of stream fish in Missouri, USA based on species environmental tolerances, rarity, range size, dispersal ability and on the average connectivity of the streams occupied by each species. These two indices differed in how environmental tolerance was classified (i.e., vulnerability to habitat alteration, changes in stream temperature, and changes to flow regimes). Environmental tolerance was classified based on measured species responses to habitat alteration, and extremes in stream temperatures and flow conditions for one index, while environmental tolerance for the second index was based on species’ traits. The indices were compared to determine if vulnerability scores differed by index or state listing status. We also evaluated the spatial distribution of species classified as vulnerable to habitat alteration, changes in stream temperature, and change in flow regimes. Vulnerability scores were calculated for all 133 species with the trait association index, while only 101 species were evaluated using the species response index, because 32 species lacked data to analyze for a response. Scores from the trait association index were greater than the species response index. This is likely due to the species response index's inability to evaluate many rare species, which generally had high vulnerability scores for the trait association index. The indices were consistent in classifying vulnerability to habitat alteration, but varied in their classification of vulnerability due to increases in stream temperature and alterations to flow regimes, likely because extremes in current climate may not fully capture future conditions and their influence on stream fish communities. Both indices showed higher mean vulnerability scores for listed species than unlisted species, which provided a coarse measure of validation. Our indices classified species identified as being in need of conservation by the state of Missouri as highly vulnerable. The distribution of vulnerable species in Missouri showed consistent patterns between indices, with the more forest-dominated, groundwater fed streams in the Ozark subregion generally having higher numbers and proportions of vulnerable species per site than subregions that were agriculturally dominated with more overland flow. These results suggest that both indices will identify similar habitats as conservation action targets despite discrepancies in the classification of vulnerable species. Our vulnerability assessment provides a framework that can be refined and used in other regions.     

Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest

Conservation strategies are often established without consideration of the impact of climate change. However, this impact is expected to threaten species and ecosystem persistence and to have dramatic effects towards the end of the 21st century. Landscape suitability for species under climate change is determined by several interacting factors including dispersal and human land use. Designing effective conservation strategies at regional scales to improve landscape suitability requires measuring the vulnerabilities of specific regions to climate change and determining their conservation capacities. Although methods for defining vulnerability categories are available, methods for doing this in a systematic, cost‐effective way have not been identified. Here, we use an ecosystem model to define the potential resilience of the Finnish forest landscape by relating its current conservation capacity to its vulnerability to climate change. In applying this framework, we take into account the responses to climate change of a broad range of red‐listed species with different niche requirements. This framework allowed us to identify four categories in which representation in the landscape varies among three IPCC emission scenarios (B1, low; A1B, intermediate; A2, high emissions): (i) susceptible (B1 = 24.7%, A1B = 26.4%, A2 = 26.2%), the most intact forest landscapes vulnerable to climate change, requiring management for heterogeneity and resilience; (ii) resilient (B1 = 2.2%, A1B = 0.5%, A2 = 0.6%), intact areas with low vulnerability that represent potential climate refugia and require conservation capacity maintenance; (iii) resistant (B1 = 6.7%, A1B = 0.8%, A2 = 1.1%), landscapes with low current conservation capacity and low vulnerability that are suitable for restoration projects; (iv) sensitive (B1 = 66.4%, A1B = 72.3%, A2 = 72.0%), low conservation capacity landscapes that are vulnerable and for which alternative conservation measures are required depending on the intensity of climate change. Our results indicate that the Finnish landscape is likely to be dominated by a very high proportion of sensitive and susceptible forest patches, thereby increasing uncertainty for landscape managers in the choice of conservation strategies.     

An integrated framework to identify wildlife populations under threat from climate change

Climate change is a major threat to global biodiversity that will produce a range of new selection pressures. Understanding species responses to climate change requires an interdisciplinary perspective, combining ecological, molecular and environmental approaches. We propose an applied integrated framework to identify populations under threat from climate change based on their extent of exposure, inherent sensitivity due to adaptive and neutral genetic variation and range shift potential. We consider intraspecific vulnerability and population‐level responses, an important but often neglected conservation research priority. We demonstrate how this framework can be applied to vertebrates with limited dispersal abilities using empirical data for the bat Plecotus austriacus. We use ecological niche modelling and environmental dissimilarity analysis to locate areas at high risk of exposure to future changes. Combining outlier tests with genotype–environment association analysis, we identify potential climate‐adaptive SNPs in our genomic data set and differences in the frequency of adaptive and neutral variation between populations. We assess landscape connectivity and show that changing environmental suitability may limit the future movement of individuals, thus affecting both the ability of populations to shift their distribution to climatically suitable areas and the probability of evolutionary rescue through the spread of adaptive genetic variation among populations. Therefore, a better understanding of movement ecology and landscape connectivity is needed for predicting population persistence under climate change. Our study highlights the importance of incorporating genomic data to determine sensitivity, adaptive potential and range shift potential, instead of relying solely on exposure to guide species vulnerability assessments and conservation planning.     

Species vulnerability to climate change: impacts on spatial conservation priorities and species representation

Climate change may shrink and/or shift plant species ranges thereby increasing their vulnerability and requiring targeted conservation to facilitate adaptation. We quantified the vulnerability to climate change of plant species based on exposure, sensitivity and adaptive capacity and assessed the effects of including these components in complementarity‐based spatial conservation prioritisation. We modelled the vulnerability of 584 native plant species under three climate change scenarios in an 11.9 million hectare fragmented agricultural region in southern Australia. We represented exposure as species' geographical range under each climate change scenario as quantified using species distribution models. We calculated sensitivity as a function of the impact of climate change on species' geographical ranges. Using a dispersal kernel, we quantified adaptive capacity as species' ability to migrate to new geographical ranges under each climate change scenario. Using Zonation, we assessed the impact of individual components of vulnerability (exposure, sensitivity and adaptive capacity) on spatial conservation priorities and levels of species representation in priority areas under each climate change scenario. The full vulnerability framework proved an effective basis for identifying spatial conservation priorities under climate change. Including different dimensions of vulnerability had significant implications for spatial conservation priorities. Incorporating adaptive capacity increased the level of representation of most species. However, prioritising sensitive species reduced the representation of other species. We conclude that whilst taking an integrated approach to mitigating species vulnerability to climate change can ensure sensitive species are well‐represented in a conservation network, this can come at the cost of reduced representation of other species. Conservation planning decisions aimed at reducing species vulnerability to climate change need to be made in full cognisance of the sensitivity of spatial conservation priorities to individual components of vulnerability, and the trade‐offs associated with focussing on sensitive species.     

Comparing and contrasting threat assessments of plant species at the global and sub-global level

Evidence-based assessments of extinction risk are established tools used to inform the conservation of plant species, and form the basis of key targets within the framework of the Global Strategy for Plant Conservation (GSPC). An overall picture of plants threat assessments is challenging due to the use of a variety of methodologies and range in scope from global to subnational. In this study, we quantify the state of progress in assessing the extinction risk of all land plants, determine the key geographic and taxonomic gaps with respect to our understanding of plant extinction risk, and evaluate the impact of different sources and methodologies on the utility of plant assessments. To this end, we have analyzed a cleaned dataset compiled from IUCN Red List of Threatened Species and Regional Red Lists. We reveal that there are assessments available for 89,810 distinct species or 25% of all accepted land plant species. However unlike with other major organismal lineages the bulk of the plant species assessments are derived from Regional Red Lists, and not the Global IUCN Red List. We demonstrate that this bias towards regional assessments results in distinct taxonomic and geographic strengths and weaknesses, and we identify substantial taxonomic and geographic gaps in the assessment coverage. With species that have been assessed in common at both global and regional levels, we explore the implications of combining threat assessments from different sources. We find that half of global and regional assessments do not agree on the exact category of extinction risk for a species. Regional assessments assign a higher risk of extinction; or underestimate extinction risk with almost equal frequency. We conclude with recommended interventions, but support the suggestion that all threat assessments should be pooled to provide more data and broaden the scope of threat assessments for monitoring progress towards GSPC targets.     

A framework for the identification of hotspots of climate change risk for mammals

As rates of global warming increase rapidly, identifying species at risk of decline due to climate impacts and the factors affecting this risk have become key challenges in ecology and conservation biology. Here, we present a framework for assessing three components of climate‐related risk for species: vulnerability, exposure and hazard. We used the relationship between the observed response of species to climate change and a set of intrinsic traits (e.g. weaning age) and extrinsic factors (e.g. precipitation seasonality within a species geographic range) to predict, respectively, the vulnerability and exposure of all data‐sufficient terrestrial non‐volant mammals (3,953 species). Combining this information with hazard (the magnitude of projected climate change within a species geographic range), we identified global hotspots of species at risk from climate change that includes the western Amazon basin, south‐western Kenya, north‐eastern Tanzania, north‐eastern South Africa, Yunnan province in China, and mountain chains in Papua‐New Guinea. Our framework identifies priority areas for monitoring climate change effects on species and directing climate mitigation actions for biodiversity.     

Beyond just sea‐level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change

Due to their position at the land‐sea interface, coastal wetlands are vulnerable to many aspects of climate change. However, climate change vulnerability assessments for coastal wetlands generally focus solely on sea‐level rise without considering the effects of other facets of climate change. Across the globe and in all ecosystems, macroclimatic drivers (e.g., temperature and rainfall regimes) greatly influence ecosystem structure and function. Macroclimatic drivers have been the focus of climate change‐related threat evaluations for terrestrial ecosystems, but largely ignored for coastal wetlands. In some coastal wetlands, changing macroclimatic conditions are expected to result in foundation plant species replacement, which would affect the supply of certain ecosystem goods and services and could affect ecosystem resilience. As examples, we highlight several ecological transition zones where small changes in macroclimatic conditions would result in comparatively large changes in coastal wetland ecosystem structure and function. Our intent in this communication is not to minimize the importance of sea‐level rise. Rather, our overarching aim is to illustrate the need to also consider macroclimatic drivers within vulnerability assessments for coastal wetlands.     

Species’ traits as predictors of range shifts under contemporary climate change: A review and meta‐analysis

A growing body of literature seeks to explain variation in range shifts using species’ ecological and life‐history traits, with expectations that shifts should be greater in species with greater dispersal ability, reproductive potential, and ecological generalization. Despite strong theoretical support for species’ traits as predictors of range shifts, empirical evidence from contemporary range shift studies remains limited in extent and consensus. We conducted the first comprehensive review of species’ traits as predictors of range shifts, collecting results from 51 studies across multiple taxa encompassing over 11,000 species’ responses for 54 assemblages of taxonomically related species occurring together in space. We used studies of assemblages that directly compared geographic distributions sampled in the 20th century prior to climate change with resurveys of distributions after contemporary climate change and then tested whether species traits accounted for heterogeneity in range shifts. We performed a formal meta‐analysis on study‐level effects of body size, fecundity, diet breadth, habitat breadth, and historic range limit as predictors of range shifts for a subset of 21 studies of 26 assemblages with sufficient data. Range shifts were consistent with predictions based on habitat breadth and historic range limit. However, body size, fecundity, and diet breadth showed no significant effect on range shifts across studies, and multiple studies reported significant relationships that contradicted predictions. Current understanding of species’ traits as predictors of range shifts is limited, and standardized study is needed for traits to be valid indicators of vulnerability in assessments of climate change impacts.     

Combining population genetics,species distribution modelling and field assessments to understand a species vulnerability to climate change

Climate change is recognized as a major threat to biodiversity. Multidisciplinary approaches that combine population genetics and species distribution modelling to assess these threats and recommend conservation actions are critical but rare. Combined, these methods provide independent verification and a more compelling case for developing conservation actions. This study integrates these data streams together with field assessments and spatial analyses to develop future genetic resource management recommendations. The study species was Callistemon teretifolius (Needle Bottlebrush), a shrub species endemic to the Mount Lofty and Flinders Ranges, South Australia, and potentially vulnerable to climate change. Chloroplast microsatellite and Amplified Fragment Length Polymorphism data were combined with species distribution modelling (MaxEnt), spatial analysis and field assessment to evaluate climate change vulnerability. Two major genetic groups were identified (Mount Lofty and Flinders Ranges). Populations in the Flinders Ranges, especially the Southern Flinders Ranges exhibited the highest genetic diversity, indicating a possible genetic refugium. Lower genetic diversity to the south in the Mount Lofty Ranges and north in the Gammon Ranges may be due to post‐glacial expansion into these areas from the Flinders Ranges or loss of alleles. Low levels of contemporary gene flow were identified, which suggests Callistemon teretifolius may have a limited capacity to respond to climate change through migration. Range restrictions were predicted for all future climates, especially in the north. It is likely that C. teretifolius will be adversely affected by climate change, due to limited gene flow, predicted range restriction and loss of suitable habitat. The Southern Flinders Ranges should be a priority for conservation because it contains the highest number of individuals and genetic diversity. We recommend monitoring and adaptive management involving restoration in the Southern Flinders Ranges, potentially incorporating genetic translocations from other areas to capture diversity, to assist C. teretifolius to adapt to climate change.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Methods matter: Different biodiversity survey methodologies identify contrasting biodiversity patterns in a human modified rainforest — A case study with amphibians

Understanding how well tropical forest biodiversity can recover following habitat change is often difficult due to conflicting assessments arising from different studies. One often overlooked potentially confounding factor that may influence assessments of biodiversity response to habitat change, is the possibility that different survey methodologies, targeting the same indicator taxon, may identify different patterns and so lead to different conclusions. Here we investigated whether two different but commonly used survey methodologies used to assess amphibian communities, pitfall trapping and nocturnal transects, indicate the same or different responses of amphibian biodiversity to historic human induced habitat change. We did so in a regenerating rainforest study site located in one of the world’s most biodiverse and important conservation areas: the Manu Biosphere Reserve. We show that the two survey methodologies tested identified contrasting biodiversity patterns in a human modified rainforest. Nocturnal transect surveys indicated biodiversity differences between forest with different human disturbance histories, whereas pitfall trap surveys suggested no differences between forest disturbance types, except for community composition. This pattern was true for species richness, diversity, overall abundance and community evenness and structure. For some fine scale metrics, such as species specific responses and abundances of family groups, both methods detected differences between disturbance types. However, the direction of differences was inconsistent between methods. We highlight that for assessments of rainforest recovery following disturbance, survey methods do matter and that different biodiversity survey methods can identify contrasting patterns in response to different types of historic disturbance. Our results contribute to a growing body of evidence that arboreal species might be more sensitive indicators than terrestrial communities.