A Comprehensive Overview of Technologies for Species and Habitat Monitoring and Conservation.

The range of technologies currently used in biodiversity conservation is staggering, with innovative uses often adopted from other disciplines and being trialed in the field. We provide the first comprehensive overview of the current (2020) landscape of conservation technology, encompassing technologies for monitoring wildlife and habitats, as well as for on-the-ground conservation management (e.g., fighting illegal activities). We cover both established technologies (routinely deployed in conservation, backed by substantial field experience and scientific literature) and novel technologies or technology applications (typically at trial stage, only recently used in conservation), providing examples of conservation applications for both types. We describe technologies that deploy sensors that are fixed or portable, attached to vehicles (terrestrial, aquatic, or airborne) or to animals (biologging), complemented with a section on wildlife tracking. The last two sections cover actuators and computing (including web platforms, algorithms, and artificial intelligence).

Insectivorous bats are less active near freeways.

Traffic disturbances (i.e. pollution, light, noise, and vibrations) often extend into the area surrounding a road creating a 'road-effect zone'. Habitat within the road-effect zone is degraded or, in severe cases, completely unsuitable for wildlife, resulting in indirect habitat loss. This can have a disproportionate impact on wildlife in highly modified landscapes, where remaining habitat is scarce or occurs predominantly along roadside reserves. In this study, we investigated the road-effect zone for insectivorous bats in highly cleared agricultural landscapes by quantifying the change in call activity with proximity to three major freeways. The activity of seven out of 10 species of bat significantly decreased with proximity to the freeway. We defined the road-effect zone to be the proximity at which call activity declined by at least 20% relative to the maximum detected activity. The overall road-effect zone for bats in this region was 307 m, varying between 123 and 890 m for individual species. Given that this road-effect zone exceeds the typical width of the roadside verges (<50 m), it is possible that much of the vegetation adjacent to freeways in this and similar landscapes provides low-quality habitat for bats. Without accounting for the road-effect zone, the amount of habitat lost or degraded due to roads is underestimated, potentially resulting in the loss of wildlife, ecosystem services and key ecosystem processes (e.g. predator-prey or plant-pollinator interactions) from the landscape. We suggest all future environmental impact assessments include quantifying the road-effect zone for sensitive wildlife, in order to best plan and mitigate the impact of roads on the environment. Mitigating the effects of new and existing roads on wildlife is essential to ensure enough high-quality habitat persists to maintain wildlife populations.

Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Managing uncertainty in movement knowledge for environmental decisions.

Species' movements affect their response to environmental change but movement knowledge is often highly uncertain. We now have well-established methods to integrate movement knowledge into conservation practice but still lack a framework to deal with uncertainty in movement knowledge for environmental decisions. We provide a framework that distinguishes two dimensions of species' movement that are heavily influenced by uncertainty: knowledge about movement and relevance of movement to environmental decisions. Management decisions can be informed by their position in this knowledge-relevance space. We then outline a framework to support decisions around (1) increasing understanding of the relevance of movement knowledge, (2) increasing robustness of decisions to uncertainties and (3) improving knowledge on species' movement. Our decision-support framework provides guidance for managing movement-related uncertainty in systematic conservation planning, agri-environment schemes, habitat restoration and international biodiversity policy. It caters to different resource levels (time and funding) so that species' movement knowledge can be more effectively integrated into environmental decisions.

Forecasting species range dynamics with process-explicit models: matching methods to applications.

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

Spatially explicit power analysis for detecting occupancy trends for multiple species.

Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species-level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long-term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera-trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision-makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods.

Inferring species richness using multispecies occupancy modeling: Estimation performance and interpretation.

Multispecies occupancy models can estimate species richness from spatially replicated multispecies detection/non-detection survey data, while accounting for imperfect detection. A model extension using data augmentation allows inferring the total number of species in the community, including those completely missed by sampling (i.e., not detected in any survey, at any site). Here we investigate the robustness of these estimates. We review key model assumptions and test performance via simulations, under a range of scenarios of species characteristics and sampling regimes, exploring sensitivity to the Bayesian priors used for model fitting. We run tests when assumptions are perfectly met and when violated. We apply the model to a real dataset and contrast estimates obtained with and without predictors, and for different subsets of data. We find that, even with model assumptions perfectly met, estimation of the total number of species can be poor in scenarios where many species are missed (>15%-20%) and that commonly used priors can accentuate overestimation. Our tests show that estimation can often be robust to violations of assumptions about the statistical distributions describing variation of occupancy and detectability among species, but lower-tail deviations can result in large biases. We obtain substantially different estimates from alternative analyses of our real dataset, with results suggesting that missing relevant predictors in the model can result in richness underestimation. In summary, estimates of total richness are sensitive to model structure and often uncertain. Appropriate selection of priors, testing of assumptions, and model refinement are all important to enhance estimator performance. Yet, these do not guarantee accurate estimation, particularly when many species remain undetected. While statistical models can provide useful insights, expectations about accuracy in this challenging prediction task should be realistic. Where knowledge about species numbers is considered truly critical for management or policy, survey effort should ideally be such that the chances of missing species altogether are low.

Little evidence of a road-effect zone for nocturnal, flying insects.

Roads and traffic may be contributing to global declines of insect populations. The ecological effects of roads often extend far into the surrounding habitat, over a distance known as the road-effect zone. The quality of habitat in the road-effect zone is generally degraded (e.g., due to edge effects, noise, light, and chemical pollution) and can be reflected in species presence, abundance, or demographic parameters. Road-effect zones have been quantified for some vertebrate species but are yet to be quantified for insects. Investigating the road-effect zone for insects will provide a better understanding of how roads impact ecosystems, which is particularly important given the role insects play as pollinators, predators, and prey for other species. We quantified the road-effect zone for nocturnal flying insects along three major freeways in agricultural landscapes in southeast Australia. We collected insects using light traps at six points along 2-km transects perpendicular to each highway (n = 17). We sorted the samples into order, and dried and weighed each order to obtain a measure of dry biomass. Using regression models within a Bayesian framework of inference, we estimated the change in biomass of each order with distance from the road, while accounting for environmental variables such as temperature, moon phase, and vegetation structure. The biomass of nine of the ten orders sampled did not change with distance from the freeway. Orthoptera (i.e., grasshoppers and crickets) was the only order whose biomass increased with distance from the freeway. From our findings, we suggest that the impacts of roads on insects are unlikely extending into the surrounding landscape over a distance of 2 km. Therefore, if there are impacts of roads on insects, these are more likely to be concentrated at the road itself, or on finer taxonomic scales such as family or genus level.

Animal movements in fire-prone landscapes.

Movement is a trait of fundamental importance in ecosystems subject to frequent disturbances, such as fire-prone ecosystems. Despite this, the role of movement in facilitating responses to fire has received little attention. Herein, we consider how animal movement interacts with fire history to shape species distributions. We consider how fire affects movement between habitat patches of differing fire histories that occur across a range of spatial and temporal scales, from daily foraging bouts to infrequent dispersal events, and annual migrations. We review animal movements in response to the immediate and abrupt impacts of fire, and the longer-term successional changes that fires set in train. We discuss how the novel threats of altered fire regimes, landscape fragmentation, and invasive species result in suboptimal movements that drive populations downwards. We then outline the types of data needed to study animal movements in relation to fire and novel threats, to hasten the integration of movement ecology and fire ecology. We conclude by outlining a research agenda for the integration of movement ecology and fire ecology by identifying key research questions that emerge from our synthesis of animal movements in fire-prone ecosystems.

Correction: Occupancy and detectability modelling of vertebrates in northern Australia using multiple sampling methods.

[This corrects the article DOI: 10.1371/journal.pone.0203304.].

Occupancy and detectability modelling of vertebrates in northern Australia using multiple sampling methods.

Understanding where species occur and how difficult they are to detect during surveys is crucial for designing and evaluating monitoring programs, and has broader applications for conservation planning and management. In this study, we modelled occupancy and the effectiveness of six sampling methods at detecting vertebrates across the Top End of northern Australia. We fitted occupancy-detection models to 136 species (83 birds, 33 reptiles, 20 mammals) of 242 recorded during surveys of 333 sites in eight conservation reserves between 2011 and 2016. For modelled species, mean occupancy was highly variable: birds and reptiles ranged from 0.01-0.81 and 0.01-0.49, respectively, whereas mammal occupancy was lower, ranging from 0.02-0.30. Of the 11 environmental covariates considered as potential predictors of occupancy, topographic ruggedness, elevation, maximum temperature, and fire frequency were retained more readily in the top models. Using these models, we predicted species occupancy across the Top End of northern Australia (293,017 km2) and generated species richness maps for each species group. For mammals and reptiles, high richness was associated with rugged terrain, while bird richness was highest in coastal lowland woodlands. On average, detectability of diurnal birds was higher per day of surveys (0.33 ± 0.09) compared with nocturnal birds per night of spotlighting (0.13 ± 0.06). Detectability of reptiles was similar per day/night of pit trapping (0.30 ± 0.09) as per night of spotlighting (0.29 ± 0.11). On average, mammals were highly detectable using motion-sensor cameras for a week (0.36 ± 0.06), with exception of smaller-bodied species. One night of Elliott trapping (0.20 ± 0.06) and spotlighting (0.19 ± 0.06) was more effective at detecting mammals than cage (0.08 ± 0.03) and pit trapping (0.05 ± 0.04). Our estimates of species occupancy and detectability will help inform decisions about how best to redesign a long-running vertebrate monitoring program in the Top End of northern Australia.

Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community.

Integrated population models (IPMs) combine data on different aspects of demography with time-series of population abundance. IPMs are becoming increasingly popular in the study of wildlife populations, but their application has largely been restricted to the analysis of single species. However, species exist within communities: sympatric species are exposed to the same abiotic environment, which may generate synchrony in the fluctuations of their demographic parameters over time. Given that in many environments conditions are changing rapidly, assessing whether species show similar demographic and population responses is fundamental to quantifying interspecific differences in environmental sensitivity and highlighting ecological interactions at risk of disruption. In this paper, we combine statistical approaches to study populations, integrating data along two different dimensions: across species (using a recently proposed framework to quantify multi-species synchrony in demography) and within each species (using IPMs with demographic and abundance data). We analyse data from three seabird species breeding at a nationally important long-term monitoring site. We combine demographic datasets with island-wide population counts to construct the first multi-species Integrated Population Model to consider synchrony. Our extension of the IPM concept allows the simultaneous estimation of demographic parameters, adult abundance and multi-species synchrony in survival and productivity, within a robust statistical framework. The approach is readily applicable to other taxa and habitats. Supplementary materials accompanying this paper appear on-line. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary materials for this article are available at 10.1007/s13253-017-0279-4.

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.

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.

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.

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 WLD's 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.

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.

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.

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.