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Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience. The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback.

Explicit articulation of values and objectives is critical Many nations have laws to identify and protect imperiled species and their ecosystems. In the United States, actions taken under the Endangered Species Act (ESA) have prevented many extinctions, but few listed species have recovered to the point where they can have the ESA protections removed ( 1 , 2 ). One reason for this [among many ( 3 )] is a shortfall in funding, raising a conundrum for agencies responsible for species recovery: Should resources be allocated toward species facing imminent extinction or species whose long-term survival can most benefit from investment? Some argue that the latter strategy is ethically unsound because it may abandon species with little hope of long-term recovery [for example, ( 4 )], even when science suggests that the former strategy may miss opportunities to prevent species from ever experiencing the risk of imminent extinction ( 2 ). We suggest that framing recovery prioritization as a resource allocation problem provides a structure to facilitate constructive debate about such important questions. We discuss here the merits of an explicit resource allocation framework and introduce a prototype decision tool [( 5 ); see supplementary materials for details] that we developed with the U.S. Fish and Wildlife Service (USFWS) to facilitate transparent and efficient recovery allocation decisions.

The Australian wildfires of 2019-20 (Black Summer) were devastating and unprecedented. These megafires burnt more than 10 million hectares, mostly of forests in southern and eastern Australia. Many of the fires were uncontrollable. These megafires affected many of Australia's most important conservation areas and severely impacted threatened species and ecological communities. They were a consequence of climate change - and offered a glimpse of how this is likely to continue to affect our future.Australia's Megafires includes contributions by more than 200 researchers and managers with direct involvement in the management and conservation of the biodiversity affected by the Black Summer wildfires. It provides a comprehensive review of the impacts of these fires on all components of biodiversity, and on Indigenous cultural values.These fires also triggered an extraordinary and highly collaborative response by governments, NGOs, Indigenous groups, scientists, landholders and others, seeking to recover the fire-affected species and environments - to restore Country. This book documents that response. It draws lessons that should be heeded to sustain that recovery and to be better prepared for the inevitable future comparable catastrophes. Such lessons are of global relevance, for wildfires increasingly threaten biodiversity and livelihoods across the globe.FEATURES:Documents the major impacts on wildlife, ecological communities, sites of biodiversity significance and Indigenous cultural values.Explores the extraordinary collaborative response to attempt to recover impacted species and environments.Provides perspectives from people involved in the fire management and recovery.Identifies necessary learnings to reduce the chance of future such catastrophes, to be better prepared and better enable recovery.Includes responses and recommendations that will be broadly applicable to comparable environmental catastrophes around the world.

Plant and animal survey detection rates are important for ecological surveys, environmental impact assessment, invasive species monitoring, and modeling species distributions. Species can be difficult to detect when rare but, in general, how detection probabilities vary with abundance is unknown. We developed a new detectability model based on the time to detection of the first individual of a species. Based on this model, the predicted detection rate is proportional to a power function of abundance with a scaling exponent between zero and one that depends on clustering of individuals. We estimated the model parameters with data from three independent datasets: searches for chenopod shrub species and coins, experimental searches for planted seedlings, and frog surveys at multiple sites in sub-tropical forests of eastern Australia. Analyses based on the detection time and detection probability suggest that detection rate increases with abundance as predicted. The model provides a way to scale detection rates to cases of low abundance when direct estimation of detection rates is often impractical.

Monitoring ground layer biomass, and therefore forage availability, is important for managing large, vertebrate herbivore populations for conservation. Remote sensing allows for frequent observations over broad spatial scales, capturing changes in biomass over the landscape and through time. In this study, we explored different satellite-derived vegetation indices (VIs) for their utility in estimating understorey biomass in semi-arid woodlands of south-eastern Australia. Relationships between VIs and understorey biomass data have not been established in these particular semi-arid communities. Managers want to use forage availability to inform cull targets for western grey kangaroos (Macropus fuliginosus), to minimise the risk that browsing poses to regeneration in threatened woodland communities when grass biomass is low. We attempted to develop relationships between VIs and understorey biomass data collected over seven seasons across open and wooded vegetation types. Generalised Linear Mixed Models (GLMMs) were used to describe relationships between understorey biomass and VIs. Total understorey biomass (live and dead, all growth forms) was best described using the Tasselled Cap (TC) greenness index. The combined TC brightness and Modified Soil Adjusted Vegetation Index (MSAVI) ranked best for live understorey biomass (all growth forms), and grass (live and dead) biomass was best described by a combination of TC brightness and greenness indices. Models performed best for grass biomass, explaining 70% of variation in external validation when predicting to the same sites in a new season. However, we found empirical relationships were not transferrable to data collected from new sites. Including other variables (soil moisture, tree cover, and dominant understorey growth form) improved model performance when predicting to new sites. Anticipating a drop in forage availability is critical for the management of grazing pressure for woodland regeneration, however, predicting understorey biomass through space and time is a challenge. Whilst remotely sensed VIs are promising as an easily-available source of vegetation information, additional landscape-scale data are required before they can be considered a cost-efficient method of understorey biomass estimation in this semi-arid landscape.

Preregistration is an open science practice which aims to improve research transparency and mitigate questionable research practices, like cherry‐picking results. It helps protect against cognitive biases, like hindsight bias, that can influence how study outcomes are interpreted. There has been little uptake of preregistration in ecology and conservation, arguably because existing preregistration templates focus on null‐hypothesis significance testing, whereas ecology and conservation often rely on different types of statistical modelling. We argue that preregistration in model‐based research in ecology and conservation is both possible and beneficial, using templates adapted for domain‐specific methodologies. We applied a user‐centred design approach to translate the concept of preregistration into model‐based research practice for ecology and conservation. To better align the internal logic of preregistration with the iterative and non‐linear process of ecological modelling, we propose, test and evaluate a methodology for ‘Adaptive Preregistration’, using a case study of modelling managed water releases (‘environmental flows modelling’) in regulated rivers for maintaining riparian vegetation condition in Victoria, Australia. This research provides a template and methodology for implementing Adaptive Preregistration of ecological models. Although we focus on ecology and conservation in this paper, the concept of Adaptive Preregistration, and the templates developed here, could be applied to model‐based research in other scientific disciplines. Modellers in ecology and conservation need no longer cry ‘but I can't preregister my research’.

The numerous environmental flows assessment methods that exist typically assume a stationary climate. Adaptive management is commonly put forward as the preferred approach for managing uncertainty and change in environmental flows. However, we contend that a simple adaptive management loop falls short of meeting the challenges posed by climate change. Rather, a fundamental rethink is required to ensure both the structure of environmental flows assessments, along with each individual technical element, actively acknowledges the multiple dimensions of change, variability and complexity in socio-ecological systems. This paper outlines how environmental flow assessments can explicitly address the uncertainty and change inherent in adaptively managing multiple values for management of environmental flows. While non-stationarity and uncertainty are well recognised in the climate literature, these have not been addressed within the structure of environmental flows methodologies. Here, we present an environmental flow assessment that is structured to explicitly consider future change and uncertainty in climate and socio-ecological values, by examining scenarios using ecological models . The environmental flow assessment methodology further supports adaptive management through the intentional integration of participatory approaches and the inclusion of diverse stakeholders. We present a case study to demonstrate the feasibility of this approach, highlighting how this methodology facilitates adaptive management. Rethinking our approach to environmental flows assessments is an important step in ensuring that environmental flows continue to work effectively as a management tool under climate change.

Ecological restoration of degraded ecosystems requires the facilitation of natural regeneration by plants, often augmented by large‐scale active revegetation. The success of such projects is highly variable. Risk factors may be readily identifiable in a general sense, but it is rarely clear how they play out individually, or in combination. We addressed this problem with a field experiment on the survival of, and browsing damage to, 1275 hand‐planted buloke (Allocasuarina luehmannii) seedlings in a nationally endangered, semi‐arid woodland community. Buloke seedlings were planted in 17 sites representing four landscape contexts and with three levels of protection from kangaroo and lagomorph browsing. We censused seedlings and measured herbivore activity four times during the first 400 d post‐planting and fitted models of mortality and browse hazard to these data using survival analysis. Increasing lagomorph activity was associated with higher mortality risk, while kangaroo activity was not. Seedling survival was lowest for each treatment within extant buloke woodland, and the highest survival rates for guarded seedlings were in locations favored by lagomorphs. Damage from browsing was nearly ubiquitous after one year for surviving unguarded seedlings, despite moderate browser activity. On average, unguarded seedlings showed a decline in height, whereas fully guarded seedlings grew 2.3 cm across the survey period. This study demonstrates buloke seedlings should be protected from browsers, even with browsers maintained at moderate to low density, and the location that maximizes survival, and possibly growth rates, is adjacent to dunes. Further work will test this heuristic in an analysis of cost‐effective revegetation strategies for this endangered community.

The U.S. Fish and Wildlife Service oversees the recovery of many species protected by the U.S. Endangered Species Act (ESA). Recent research suggests that a structured approach to allocating conservation resources could increase recovery outcomes for ESA listed species. Quantitative approaches to decision support can efficiently allocate limited financial resources and maximize desired outcomes. Yet, developing quantitative decision support under real-world constraints is challenging. Approaches that pair research teams and end-users are generally the most effective. However, co-development requires overcoming “hurdles” that can arise because of differences in the mental models of the co-development team. These include perceptions that: (1) scarce funds should be spent on action, not decision support; (2) quantitative approaches are only useful for simple decisions; (3) quantitative tools are inflexible and prescriptive black boxes; (4) available data are not good enough to support decisions; and (5) prioritization means admitting defeat. Here, we describe how we addressed these misperceptions during the development of a prototype resource allocation decision support tool for understanding trade-offs in U.S. endangered species recovery. We describe how acknowledging these hurdles and identifying solutions enabled us to progress with development. We believe that our experience can assist other applications of developing quantitative decision support for resource allocation.

Environmental flows (e-flows) management takes place within a complex social-ecological system, necessitating the involvement of diverse stakeholders and an appreciation of a range of perspectives and knowledge types. It is widely accepted that incorporating participatory methods into environmental flows decision-making will allow stakeholders to become meaningfully involved, improving potential solutions, and fostering social legitimacy. However, due to substantial structural barriers, implementing participatory approaches can be difficult for water managers. This paper assesses the effectiveness of an e-flows methodology that combines elements of structured decision-making and participatory modeling, whilst constrained by project resources. Three process-based objectives were identified by the group at the start of the process: improving transparency, knowledge exchange, and community ownership. We evaluated the success of the approach according to those objectives using semi-structured interviews and thematic analysis. In evaluating how well the participatory approach achieved the process objectives, we found that at least 80% of respondents expressed positive sentiment in every category (n = 15). We demonstrate that the values-based process objectives defined by the participant group are an effective tool for evaluating participatory success. This paper highlights that participatory approaches can be effective even in resource-constrained environments when the process is adapted to fit the decision-making context.