José J. Lahoz-Monfort

Ignoring imperfect detection in biological surveys is dangerous: a response to 'fitting and interpreting occupancy models'.

In a recent paper, Welsh, Lindenmayer and Donnelly (WLD) question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLDs claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.

Statistical approaches to account for false-positive errors in environmental DNA samples.

Environmental DNA (eDNA) sampling is prone to both false‐positive and false‐negative errors. We review statistical methods to account for such errors in the analysis of eDNA data and use simulations to compare the performance of different modelling approaches. Our simulations illustrate that even low false‐positive rates can produce biased estimates of occupancy and detectability. We further show that removing or classifying single PCR detections in an ad hoc manner under the suspicion that such records represent false positives, as sometimes advocated in the eDNA literature, also results in biased estimation of occupancy, detectability and false‐positive rates. We advocate alternative approaches to account for false‐positive errors that rely on prior information, or the collection of ancillary detection data at a subset of sites using a sampling method that is not prone to false‐positive errors. We illustrate the advantages of these approaches over ad hoc classifications of detections and provide practical advice and code for fitting these models in maximum likelihood and Bayesian frameworks. Given the severe bias induced by false‐negative and false‐positive errors, the methods presented here should be more routinely adopted in eDNA studies.

When do we need more data? A primer on calculating the value of information for applied ecologists

Summary Applied ecologists continually advocate further research, under the assumption that obtaining more information will lead to better decisions. Value of information (VoI) analysis can be used to quantify how additional information may improve management outcomes: despite its potential, this method is still underused in environmental decision-making. We provide a primer on how to calculate the VoI and assess whether reducing uncertainty will change a decision. Our aim is to facilitate the application of VoI by managers who are not familiar with decision-analytic principles and notation, by increasing the technical accessibility of the tool. Calculating the VoI requires explicit formulation of management objectives and actions. Uncertainty must be clearly structured and its effects on management outcomes evaluated. We present two measures of the VoI. The expected value of perfect information is a calculation of the expected improvement in management outcomes that would result from access to perfect knowledge. The expected value of sample information calculates the improvement in outcomes expected by collecting a given sample of new data. We guide readers through the calculation of VoI using two case studies: (i) testing for disease when managing a frog species and (ii) learning about demographic rates for the reintroduction of an endangered turtle. We illustrate the use of Bayesian updating to incorporate new information. The VoI depends on our current knowledge, the quality of the information collected and the expected outcomes of the available management actions. Collecting information can require significant investments of resources; VoI analysis assists managers in deciding whether these investments are justified.

Population Status of a Cryptic Top Predator: An Island-Wide Assessment of Tigers in Sumatran Rainforests

Large carnivores living in tropical rainforests are under immense pressure from the rapid conversion of their habitat. In response, millions of dollars are spent on conserving these species. However, the cost-effectiveness of such investments is poorly understood and this is largely because the requisite population estimates are difficult to achieve at appropriate spatial scales for these secretive species. Here, we apply a robust detection/non-detection sampling technique to produce the first reliable population metric (occupancy) for a critically endangered large carnivore; the Sumatran tiger (Panthera tigris sumatrae). From 2007–2009, seven landscapes were surveyed through 13,511 km of transects in 394 grid cells (17×17 km). Tiger sign was detected in 206 cells, producing a naive estimate of 0.52. However, after controlling for an unequal detection probability (where p = 0.13±0.017; ±S.E.), the estimated tiger occupancy was 0.72±0.048. Whilst the Sumatra-wide survey results gives cause for optimism, a significant negative correlation between occupancy and recent deforestation was found. For example, the Northern Riau landscape had an average deforestation rate of 9.8%/yr and by far the lowest occupancy (0.33±0.055). Our results highlight the key tiger areas in need of protection and have led to one area (Leuser-Ulu Masen) being upgraded as a ‘global priority’ for wild tiger conservation. However, Sumatra has one of the highest global deforestation rates and the two largest tiger landscapes identified in this study will become highly fragmented if their respective proposed roads networks are approved. Thus, it is vital that the Indonesian government tackles these threats, e.g. through improved land-use planning, if it is to succeed in meeting its ambitious National Tiger Recovery Plan targets of doubling the number of Sumatran tigers by 2022.

Deep-sea diversity patterns are shaped by energy availability

The deep ocean is the largest and least-explored ecosystem on Earth, and a uniquely energy-poor environment. The distribution, drivers and origins of deep-sea biodiversity remain unknown at global scales. Here we analyse a database of more than 165,000 distribution records of Ophiuroidea (brittle stars), a dominant component of sea-floor fauna, and find patterns of biodiversity unlike known terrestrial or coastal marine realms. Both patterns and environmental predictors of deep-sea (2,000–6,500 m) species richness fundamentally differ from those found in coastal (0–20 m), continental shelf (20–200 m), and upper-slope (200–2,000 m) waters. Continental shelf to upper-slope richness consistently peaks in tropical Indo-west Pacific and Caribbean (0–30°) latitudes, and is well explained by variations in water temperature. In contrast, deep-sea species show maximum richness at higher latitudes (30–50°), concentrated in areas of high carbon export flux and regions close to continental margins. We reconcile this structuring of oceanic biodiversity using a species–energy framework, with kinetic energy predicting shallow-water richness, while chemical energy (export productivity) and proximity to slope habitats drive deep-sea diversity. Our findings provide a global baseline for conservation efforts across the sea floor, and demonstrate that deep-sea ecosystems show a biodiversity pattern consistent with ecological theory, despite being different from other planetary-scale habitats.

Guidelines for Using Movement Science to Inform Biodiversity Policy

Substantial advances have been made in our understanding of the movement of species, including processes such as dispersal and migration. This knowledge has the potential to improve decisions about biodiversity policy and management, but it can be difficult for decision makers to readily access and integrate the growing body of movement science. This is, in part, due to a lack of synthesis of information that is sufficiently contextualized for a policy audience. Here, we identify key species movement concepts, including mechanisms, types, and moderators of movement, and review their relevance to (1) national biodiversity policies and strategies, (2) reserve planning and management, (3) threatened species protection and recovery, (4) impact and risk assessments, and (5) the prioritization of restoration actions. Based on the review, and considering recent developments in movement ecology, we provide a new framework that draws links between aspects of movement knowledge that are likely the most relevant to each biodiversity policy category. Our framework also shows that there is substantial opportunity for collaboration between researchers and government decision makers in the use of movement science to promote positive biodiversity outcomes.

Breeding together: modeling synchrony in productivity in a seabird community

With environmental conditions changing rapidly, there is a need to move beyond single-species models and consider how communities respond to environmental drivers. We present a modeling approach that allows estimation of multispecies synchrony in productivity, or its components, and the contribution of environmental covariates as synchronizing and desynchronizing agents. We apply the model to long-term breeding success data for five seabird species at a North Atlantic colony. Our Bayesian analysis reveals varying degrees of synchrony in overall productivity, with a common signal indicating a significant decline in productivity between 1986 and 2009. Productivity in seabirds reflects conditions in the marine ecosystem so the estimated synchronous component is a useful indicator of local marine environment health. For the two species for which we have most data, the environmental contribution to overall productivity synchrony is driven principally by effects operating at the chick stage rather than during incubation. Our results emphasize the importance of studying together species that coexist in a community. The framework, which accommodates interspecific clutch-size variation, is readily applicable to any species assemblage in any ecosystem where long-term productivity data are available.

Exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival

1. Long-term monitoring programmes often involve substantial input of skilled staff time. In mark–recapture studies, considerable effort is usually devoted to both marking and recapturing/resighting individuals. Given increasing budgetary constraints, it is essential to streamline field protocols to minimize data redundancy while still achieving targets such as detecting trends or ecological effects. 2. We evaluated different levels of field effort investment in marking and resighting individuals by resampling existing mark–recapture–recovery data to construct plausible scenarios of changes in field protocols. We demonstrate the method with 26 years data from a common guillemot Uria aalge monitoring programme at a major North Sea colony. We also assess the impact of stopping the ringing of chicks on our ability to study population demography using integrated population models (IPM) fitted to data including information on breeding adults. Different data sets were removed artificially to explore the ability to compensate for missing data. 3. Current ringing effort at this colony appears adequate but resighting effort could be halved while still maintaining the capacity to monitor first-year survival and detect the effect of hatch date on survival prospects. 4. The IPM appears robust for estimating survival, productivity or abundance of the breeding population, but has limited capacity to recover year-specific first-year survival when chick data are omitted. If productivity were not monitored, the inclusion of chick data would be essential to estimate it, albeit imprecisely. 5. Synthesis and applications: Post-study evaluation can help streamline existing long-term environmental monitoring programmes. To our knowledge, this study is the first use of data thinning of existing mark–recapture–recovery data to identify potential field effort reductions. We also highlight how alternative monitoring scenarios can be evaluated with integrated population models when data are collected on different aspects of demography and abundance. When effort reduction is necessary, both approaches provide decision-support tools for adjusting field protocols to collect demographic data. The framework has broad applicability to other taxa and demographic parameters, provided suitable long-term data are available, and we discuss its use in different contexts

Minimizing the cost of keeping options open for conservation in a changing climate

Policy documents advocate that managers should keep their options open while planning to protect coastal ecosystems from climate-change impacts. However, the actual costs and benefits of maintaining flexibility remain largely unexplored, and alternative approaches for decision making under uncertainty may lead to better joint outcomes for conservation and other societal goals. For example, keeping options open for coastal ecosystems incurs opportunity costs for developers. We devised a decision framework that integrates these costs and benefits with probabilistic forecasts for the extent of sea-level rise to find a balance between coastal ecosystem protection and moderate coastal development. Here, we suggest that instead of keeping their options open managers should incorporate uncertain sea-level rise predictions into a decision-making framework that evaluates the benefits and costs of conservation and development. In our example, based on plausible scenarios for sea-level rise and assuming a risk-neutral decision maker, we found that substantial development could be accommodated with negligible loss of environmental assets. Characterization of the Pareto efficiency of conservation and development outcomes provides valuable insight into the intensity of trade-offs between development and conservation. However, additional work is required to improve understanding of the consequences of alternative spatial plans and the value judgments and risk preferences of decision makers and stakeholders.

Dealing with false‐positive and false‐negative errors about species occurrence at multiple levels

Summary Accurate knowledge of species occurrence is fundamental to a wide variety of ecological, evolutionary and conservation applications. Assessing the presence or absence of species at sites is often complicated by imperfect detection, with different mechanisms potentially contributing to false-negative and/or false-positive errors at different sampling stages. Ambiguities in the data mean that estimation of relevant parameters might be confounded unless additional information is available to resolve those uncertainties. Here, we consider the analysis of species detection data with false-positive and false-negative errors at multiple levels. We develop and examine a two-stage occupancy-detection model for this purpose. We use profile likelihoods for identifiability analysis and estimation, and study the types of additional data required for reliable estimation. We test the model with simulated data, and then analyse data from environmental DNA (eDNA) surveys of four Australian frog species. In our case study, we consider that false positives may arise due to contamination at the water sample and quantitative PCR-sample levels, whereas false negatives may arise due to eDNA not being captured in a field sample, or due to the sensitivity of laboratory tests. We augment our eDNA survey data with data from aural surveys and laboratory calibration experiments. We demonstrate that the two-stage model with false-positive and false-negative errors is not identifiable if only survey data prone to false positives are available. At least two sources of extra information are required for reliable estimation (e.g. records from a survey method with unambiguous detections, and a calibration experiment). Alternatively, identifiability can be achieved by setting plausible bounds on false detection rates as prior information in a Bayesian setting. The results of our case study matched our simulations with respect to data requirements, and revealed false-positive rates greater than zero for all species. We provide statistical modelling tools to account for uncertainties in species occurrence survey data when false negatives and false positives could occur at multiple sampling stages. Such data are often needed to support management and policy decisions. Dealing with these uncertainties is relevant for traditional survey methods, but also for promising new techniques, such as eDNA sampling.