Species distribution models are widely used to identify potential and high-quality habitat of endangered species to inform conservation decisions. However, their usefulness is constrained by the amount and quality of biodiversity data and the approaches for dealing with data deficiencies. Presence-only data, used in presence/background modelling methods, are widely available but are often affected by sampling bias. Presence/absence modelling methods are less affected by biases, but data are less common. We modelled the distribution of a widely distributed, endangered species from Australia - the greater glider - and tested how predictions were influenced by data treatment and modelling framework. We collated available species data and fitted generalized linear models and boosted regression trees using presence/absence data, as well as using an augmented dataset that included additional presences alongside absences inferred from survey data. We also fitted presence/background models, adopting three common strategies for bias correction. We compared model performance quantitatively through evaluation metrics calculated internally and on held out data, and qualitatively by identifying areas of agreement of spatial predictions. We found that presence/background models with bias correction performed better than not corrected, though evaluation metrics did not favour a single strategy. Presence/absence models outperformed presence/background models in comparable metrics and delivered different spatial predictions. Importantly, differences in spatial predictions between models had the potential to substantially alter decisions about where to protect high-quality habitat. The approach to inferring absences proved useful, as models fitted with these outperformed all other models. Dealing with sampling bias requires additional time and data management strategies, but we found that the time invested allowed improvement of models and more reliable predictions. Our results suggest that ancillary occurrence data and careful data handling can improve both presence/background and presence/absence models.

AimTo assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data. LocationAustralian Wet Tropics (AWT), Ontario, Canada (CAN), north-east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI). Time periodMost species data were collected between 1950 and 2000. Major taxa studiedBirds, mammals, plants and reptiles. MethodsWe compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence-background data for 171 species and tested on presence-absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75-km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision-recall gain curves, and correlation) and assessed the statistical significance of the differences between them. Results The ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre-selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest. Main conclusionsWhilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas.

Ecological models used to forecast range change (range change models; RCM) have recently diversified to account for a greater number of ecological and observational processes in pursuit of more accurate and realistic predictions. Theory suggests that process‐explicit RCMs should generate more robust forecasts, particularly under novel environmental conditions. RCMs accounting for processes are generally more complex and data hungry, and so, require extra effort to build. Thus, it is necessary to understand when the effort of building a more realistic model is likely to generate more reliable forecasts. Here, we review the literature to explore whether process‐explicit models have been tested through benchmarking their temporal predictive performance (i.e. their predictive performance when transferred in time) and model transferability (i.e. their ability to keep their predictive performance when transferred to generate predictions into a different time) against simpler models, and highlight the gaps between the rapid development of process‐explicit RCMs and the testing of their potential improvements. We found that, out of five ecological processes (dispersal, demography, physiology, evolution, species interactions) and two observational processes (sampling bias, imperfect detection) that may influence reliability of forecasts, only the effects of dispersal, demography and imperfect detection have been benchmarked using temporally‐independent datasets. Only nine out of twenty‐nine process‐explicit model types have been tested to assess whether accounting for processes improves temporal predictive performance. We found no benchmarks assessing model transferability. We discuss potential reasons for the lack of empirical validation of process‐explicit models. Considering these findings, we propose an expanded research agenda to properly test the performance of process‐explicit RCMs, and highlight some opportunities to fill the gaps by suggesting models to be benchmarked using existing historical datasets.

1. While there has been substantial literature on the evaluation of predictions from single species distribution models, the topic of prediction has only recently begun to be addressed for joint species distribution models (JSDMs). These studies have covered only limited aspects of prediction: limited selection of models being compared, limited number of evaluation metrics, and/or not comparing the different prediction types available to JSDMs. 2. In this study, we perform a large-scale comparison of the predictive performance of eight model types: two stacked species distribution models (SSDMs) and six JSDMs. We fit these models to 22 real and simulated datasets, make four types of JSDM predictions, and evaluate up to 32 metrics from five different classes that quantify different aspects of performance of predictions about species distributions and the community assemblage process. 3. We found that likelihood-based metrics indicated the JSDMs were better fit to the data than the standard SSDM, but most other metric classes showed the SSDM outperforming the JSDMs by generally small amounts. The spatial and non-spatial implementations of the hierarchical multivariate probit regression model with latent factors typically performed better than the other JSDMs, but overall still performed worse than the SSDM. The SSDM predictions constrained with the spatially-explicit species assemblage modelling framework (SESAM) consistently outperformed both the standard SSDM and all JSDMs for both species- and community-level metrics. 4. Our results indicate that despite the additional inference they provide about the community assemblage process by accounting for the residual association between species, JSDMs generally yield worse predictions than stacked single species models when evaluated at either the species or community level. The performance of the SESAM framework suggests that exploring similar approaches to constrain JSDM predictions is an interesting future avenue of research.

New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community observations at high spatial, temporal, and taxonomic resolution and at large spatial scale ('many rows and many columns') has been widely discussed, but so far, there has been little integration of these data with ecological models and theory. Here, we review these developments and highlight emerging solutions, focusing on statistical methods for analyzing novel community data, in particular joint species distribution models; the new ecological questions that can be answered with these data; and the potential implications of these developments for policy and conservation.

The replacement of natural areas with forestry plantations is a worldwide expanding process with direct consequences for biodiversity and ecosystem functionality. In the Mediterranean region, Eucalyptus spp. plantations are widespread, forming monospecific landscapes that in Portugal dominate most of its forested areas. The reduction in the availability of native habitats induces important challenges to native wildlife, namely changes in habitat use patterns and behavior. In this study, we evaluated the influence of Eucalyptus globulus exotic plantations on the occupancy patterns of the red fox (Vulpes vulpes L.), a mesocarnivore widely distributed in the Palearctic, and we examined potential drivers shaping those patterns. We conducted camera trapping surveys in Central Portugal (Southwestern Europe), in eight 16 km2 grids, and analyzed the resulting occurrence data accounting for imperfect detection. Our analysis revealed a preference of the red fox for native vegetation over non-native plantations and avoidance of areas with higher human disturbance. Our data suggest that the current structure of exotic plantations can have a negative impact on species occurrence, even for generalist and resilient species such as the red fox. By gaining insight into landscape structures that promotes fox occupancy, our research can contribute to the development and implementation of more integrative management measures aiming to promote the presence and conservation of mesocarnivores in Eucalyptus dominated landscapes while ensuring sustainable exploitation of these plantations.

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence-only records (available through digital databases). There have been many studies comparing the performance of alternative algorithms for modeling presence-only data. Among these, a 2006 paper from Elith and colleagues has been particularly influential in the field, partly because they used several novel methods (at the time) on a global data set that included independent presence-absence records for model evaluation. Since its publication, some of the algorithms have been further developed and new ones have emerged. In this paper, we explore patterns in predictive performance across methods, by reanalyzing the same data set (225 species from six different regions) using updated modeling knowledge and practices. We apply well-established methods such as generalized additive models and MaxEnt, alongside others that have received attention more recently, including regularized regressions, point-process weighted regressions, random forests, XGBoost, support vector machines, and the ensemble modeling framework biomod. All the methods we use include background samples (a sample of environments in the landscape) for model fitting. We explore impacts of using weights on the presence and background points in model fitting. We introduce new ways of evaluating models fitted to these data, using the area under the precision-recall gain curve, and focusing on the rank of results. We find that the way models are fitted matters. The top method was an ensemble of tuned individual models. In contrast, ensembles built using the biomod framework with default parameters performed no better than single moderate performing models. Similarly, the second top performing method was a random forest parameterized to deal with many background samples (contrasted to relatively few presence records), which substantially outperformed other random forest implementations. We find that, in general, nonparametric techniques with the capability of controlling for model complexity outperformed traditional regression methods, with MaxEnt and boosted regression trees still among the top performing models. All the data and code with working examples are provided to make this study fully reproducible.

Abstract 1. The establishment of new botanic gardens in tropical regions highlights a need for weed risk assessment tools suitable for tropical ecosystems. The relevance of plant traits for invasion into tropical rainforests has not been well studied. 2. Working in and around four botanic gardens in Indonesia where 590 alien species have been planted, we estimated the effect of four plant traits, plus time since species introduction, on: (a) the naturalization probability and (b) abundance (density) of naturalized species in adjacent native tropical rainforests; and (c) the distance that naturalized alien plants have spread from the botanic gardens. 3. We found that specific leaf area (SLA) strongly differentiated 23 naturalized from 78 non‐naturalized alien species (randomly selected from 577 non‐naturalized species) in our study. These trends may indicate that aliens with high SLA, which had a higher probability of naturalization, benefit from at least two factors when establishing in tropical forests: high growth rates and occupation of forest gaps. Naturalized aliens had high SLA and tended to be short. However, plant height was not significantly related to species' naturalization probability when considered alongside other traits. 4. Alien species that were present in the gardens for over 30 years and those with small seeds also had higher probabilities of becoming naturalized, indicating that garden plants can invade the understorey of closed canopy tropical rainforests, especially when invading species are shade tolerant and have sufficient time to establish. 5. On average, alien species that were not animal dispersed spread 78 m further into the forests and were more likely to naturalize than animal‐dispersed species. We did not detect relationships between the measured traits and estimated density of naturalized aliens in the adjacent forests. 6. Synthesis: Traits were able to differentiate alien species from botanic gardens that naturalized in native forest from those that did not; this is promising for developing trait‐based risk assessment in the tropics. To limit the risk of invasion and spread into adjacent native forests, we suggest tropical botanic gardens avoid planting alien species with fast carbon capture strategies and those that are shade tolerant.

Open-access occurrence data are useful for studying spatial patterns of fungi, but often have quality issues. These include errors in taxonomy and geo-coordinates, and incomplete coverage across areas and taxonomic groups. We identify 15 quality issues that can lead to incorrect biogeographic inference, and develop a reproducible pipeline that flags and removes problematic entries. This pipeline tests accuracy of geographic records and names. Then, if information on non-native status is unavailable or unreliable, it detects non-native species via a predictive model. Finally, it identifies spatial and environmental outliers and removes them when biologically improbable. We test the pipeline by cleaning data for Australian fungi, with 251,642 records retained after cleaning the initial 1,034,601 records. Exploratory analysis showed that the cleaned data is useful for analyses such as biogeographic regionalisation, but recording gaps and lack of saturation in collection effort also caution that more surveys are needed to improve collection completeness.

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species’ occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long‐term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Line‐transect distance sampling is widely used to estimate population densities using distances of observed targets from transect lines to model detectability. When the target taxa are high density, the frequent measuring of distances may make the method seem impractical. We present a method that improves the efficiency of distance sampling when the target species occurs at high density. Only a proportion of targets are measured to model the detection function, and the time saved on the survey is then used to cover a longer total length of transect and accrue a larger ‘count only’ sample. This approach can improve the precision of the population density estimate when the cost of measuring the distance to a detected target is more than half the cost of walking to the next target. We find the optimal proportion of distances to measure that minimises the variance of the density estimate for a fixed survey budget. We quantify how much this optimised strategy increases the precision of the density estimate compared with conventional line‐transect distance sampling. We then use simulated distance sampling data to test our expressions, and illustrate circumstances under which the optimised approach would be beneficial using distance sampling data on high‐density plants. The simulations indicate that the optimised method delivers benefits in precision, but the magnitude of the benefit is lower than predicted from our expressions, which are based on an asymptotic approximation of the variance. We apply an adjustment to the predicted benefit equation to account for this difference, and show that, in all three plant case studies, the optimised approach could improve the precision gained from a distance sampling survey between 20% and 50%. This new approach could broaden the ecological contexts in which distance sampling is applied, to include estimation of densities of abundant taxa where plots are conventionally used. The method may have interesting applications for other survey types, including multispecies surveys or those using cues or signs that occur at high density.

Joint species distribution models (JSDMs) simultaneously model the distributions of multiple species, while accounting for residual co-occurrence patterns. Despite increasing adoption of JSDMs in the literature, the question of how to define and evaluate JSDM predictions has only begun to be explored. We define four different JSDM prediction types that correspond to different aspects of species distribution and community assemblage processes. Marginal predictions are environment-only predictions akin to predictions from single-species models; joint predictions simultaneously predict entire community assemblages; and conditional marginal and conditional joint predictions are made at the species or assemblage level, conditional on the known occurrence state of one or more species at a site. We define five different classes of metrics that can be used to evaluate these types of predictions: threshold-dependent, threshold-independent, community dissimilarity, species richness and likelihood metrics. We illustrate different prediction types and evaluation metrics using a case study in which we fit a JSDM to a frog occurrence dataset collected in Melbourne, Australia. Joint species distribution models present opportunities to investigate the facets of species distribution and community assemblage processes that are not possible to explore with single-species models. We show that there are a variety of different metrics available to evaluate JSDM predictions, and that choice of prediction type and evaluation metric should closely match the questions being investigated.

Accurately predicting species ranges is a primary goal of ecology. Demographic distribution models (DDMs), which correlate underlying vital rates (e.g. survival and reproduction) with environmental conditions, can potentially predict species ranges through time and space. However, tests of DDM accuracy across wide ranges of species' life histories are surprisingly lacking. Using simulations of 1.5 million hypothetical species' range dynamics, we evaluated when DDMs accurately predicted future ranges, to provide clear guidelines for the use of this emerging approach. We limited our study to deterministic demographic models ignoring density dependence, since these models are the most commonly used in the literature. We found that density-independent DDMs overpredicted extinction if populations were near carrying capacity in the locations where demographic data were available. However, DDMs accurately predicted species ranges if demographic data were limited to sites with mean initial abundance less than one half of carrying capacity. Additionally, the DDMs required demographic data from at least 25 sites, over a short time-interval (< 10 time-steps), as populations initially below carrying capacity can saturate in long-term studies. For species with demographic data from many low density sites, DDMs predicted occurrence more accurately than correlative species distribution models (SDMs) in locations where the species eventually persisted, but not where the species went extinct. These results were insensitive to differences in simulated dispersal, levels of environmental stochasticity, the effects of the environmental variables and the functional forms of density dependence. Our findings suggest that deterministic, density-independent DDMs are appropriate for applications where locating all possible sites the species might occur in is prioritized over reducing false presence predictions in absent sites. This makes DDMs a promising tool for mapping invasion risk. However, demographic data are often collected at sites where a species is abundant. Density-independent DDMs are inappropriate in this case.

The random forest (RF) algorithm is an ensemble of classification or regression trees and is widely used, including for species distribution modelling (SDM). Many researchers use implementations of RF in the R programming language with default parameters to analyse species presence‐only data together with ‘background' samples. However, there is good evidence that RF with default parameters does not perform well for such ‘presence‐background' modelling. This is often attributed to the disparity between the number of presence and background samples, also known as 'class imbalance', and several solutions have been proposed. Here, we first set the context: the background sample should be large enough to represent all environments in the region. We then aim to understand the drivers of poor performance of RF when models are fitted to presence‐only species data alongside background samples. We show that 'class overlap' (where both classes occur in the same environment) is an important driver of poor performance, alongside class imbalance. Class overlap can even degrade performance for presence–absence data. We explain, test and evaluate suggested solutions. Using simulated and real presence‐background data, we compare performance of default RF with other weighting and sampling approaches. Our results demonstrate clear evidence of improvement in the performance of RFs when techniques that explicitly manage imbalance are used. We show that these either limit or enforce tree depth. Without compromising the environmental representativeness of the sampled background, we identify approaches to fitting RF that ameliorate the effects of imbalance and overlap and allow excellent predictive performance. Understanding the problems of RF in presence‐background modelling allows new insights into how best to fit models, and should guide future efforts to best deal with such data.

Aim The world's forested area has been declining, especially in developing countries. In contrast, forest plantations are increasing, particularly exoticEucalyptusplantations, which cover nowadays over 20 million ha worldwide. This global landscape change affects native communities, especially those at higher trophic levels that are affected by bottom-up cascading effects, such as carnivores. We seek to identify the general life-history traits of mammalian carnivore species that use exoticEucalyptusplantations. Location We reviewed 55 studies reporting carnivore presence inEucalyptusplantations worldwide. Methods We consider seven species life-history traits (generation length, social behaviour, body mass, energetic trophic level, diet diversity, habitat generalist/specialist and locomotion mode) as candidate drivers. We used generalized linear mixed models, with life-history traits as fixed factors, and study as well as carnivore species as random factors. We obtained the carnivore occurrence data from the literature (detection of 42 different species, from seven families). We considered non-detected species those with an IUCN Red List of Threatened Species estimated distribution range overlapping with the study areas, but not recorded by the studies. Results While we found no evidence of an effect of any of the other life-history traits tested, our modelling procedure indicated that habitat generalist species are more likely to useEucalyptusforests than specialist species. Main conclusions Our results, therefore, confirm an impoverishment of predator communities in disturbed environments, with the exclusion of the most specialist predators, leading to fragmentation of their populations and, ultimately contributing to their local extinction. The local extinction of specialist carnivores may lead to "functional homogenization" of communities within plantations, modifying ecosystem functioning with a negative impact on plantations' productivity, profitability and services.

The score test statistic from the observed information is easy to compute numerically. Its large sample distribution under the null hypothesis is well known and is equivalent to that of the score test based on the expected information, the likelihood-ratio test and the Wald test. However, several authors have noted that under the alternative hypothesis this no longer holds and in particular the score statistic from the observed information can take negative values. We extend the anthology on the score test to a problem of interest in ecology when studying species occurrence. This is the comparison of two zero-inflated binomial random variables from two independent samples under imperfect detection. An analysis of eigenvalues associated with the score test in this setting assists in understanding why using the observed information matrix in the score test can be problematic. We demonstrate through a combination of simulations and theoretical analysis that the power of the score test calculated under the observed information decreases as the populations being compared become more dissimilar. In particular, the score test based on the observed information is inconsistent. Finally, we propose a modified rule that rejects the null hypothesis when the score statistic is computed using the observed information is negative or is larger than the usual chi-square cut-off. In simulations in our setting this has power that is comparable to the Wald and likelihood ratio tests and consistency is largely restored. Our new test is easy to use and inference is possible. Supplementary material for this article is available online as per journal instructions.

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision‐making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post‐2020 global biodiversity framework of the Convention on Biological Diversity.

As we sit in the vortex of the Covid-19 outbreak, individual energies are focused on staying safe and juggling the personal, social and financial impacts of the pandemic and political responses to it. These impacts are profoundly re-shaping our lives, with many commentators suggesting that 'normality' will be permanently redefined for all sectors of society. The future is not clear because the maelstrom is so intense that it is unlikely that the dust will settle any time soon. This pandemic will be one of the major game changers for humanity in the 21st century. The conservation impacts are set to be huge, and this is an understatement. It is remarkable how little past attention has been given to identifying the conservation impacts of human responses to pandemics and preparing for these, especially given considerable investment in global biodiversity and conservation-focused horizon scanning exercises over the last decade (e.g. Sutherland et al., 2020). Conservation scientists, practitioners and policy-makers must urgently address this lack of preparation and innovate solutions to confront the challenges arising from the radically altered economics, attitudes and behaviours imposed by Covid-19. Our job is to think creatively and collaboratively with other sectors of society to ensure that recent progress in implementing effective conservation and protection of nature is not lost. We must also insist that conservationists contribute to re-shaping the future post-Covid-19 world, to ensure that potential benefits to nature conservation and protection are realized. We identify three broad challenges and a diverse set of potential positive developments that require urgent attention and strategy development. We cannot afford to sit back and wait to see what happens as the new world emerges, or to be unprepared when the next pandemic hits. Conservation action works. In many cases, we already know how to reduce species' extinction rates (Hoffmann et al., 2010; Monroe et al., 2019) and make progress in ecosystem restoration (Crouzeilles et al., 2017; Strassburg et al., 2019). Funding, coupled with its effective use, is key to these successes. As economies contract in response to the fight against Covid-19, all areas of expenditure by individuals, industry and government will be squeezed. Previous investments in conservation are unlikely to be maintained, and there seem to be vanishingly small prospects of substantial increases in investment to the levels required to meet globally agreed conservation targets (McCarthy et al., 2012). Conservation funding shortfalls will be compounded by increased competition with humanitarian-focused charities and the collapse of the global ecotourism market due to Covid-19 travel restrictions. Countless individual decisions on how to replace livelihoods previously gained via ecotourism will need to be made – and in many cases may well result in shifts back towards more environmentally destructive practices. In the longer term, austerity measures that are likely to be introduced once Covid-19 is under control are bound to further reduce investment in conservation agencies and conservation research. The glaring paradox with these financial constraints is that intact functioning ecosystems, with low rates of conversion from natural habitats, are critical for delivering ecosystem services including regulating zoonotic disease outbreaks and providing other health benefits (Cunningham et al., 2017; Faust et al., 2018; MacDonald & Mordecai, 2019). Conservationists must make the health and well-being benefits of the natural world increasingly clear and advocate that responses to the current crisis must have minimal impact on longer-term disease risks associated with environmental destruction. Mitigating the risk of future zoonotic diseases absolutely requires better care of the natural world. With political attention focused on mitigating Covid-19's societal and economic impacts, there is mounting evidence of everything else that troubles the world being ignored. For example, with climate change, the 26th session of the Conference of the Parties has been postponed until 2021.1 Despite recent short-term falls in greenhouse gas emissions due to economic shutdowns, this still significantly eats into the narrow time margin available to keep climate change within 'safe' limits (Lamontagne et al., 2019). Dates for the UN 2020 Biodiversity Conference, and some associated working groups, have also been pushed back – delaying the critical task of agreeing the post-2020 global biodiversity framework.2 At regional levels, implementing the EU's 'farm to fork' strategy, aimed at reducing agricultural pollution, has been further delayed in response to pressures on the farming sector. UK cities are postponing clean air zone plans due to the Covid-19 crisis.3 There is mounting evidence that illegal wildlife persecution is growing, including increases in raptor persecution in Europe,4 illegal hunting in Malta and bushmeat and ivory poaching in Africa and Asia.5 In Brazil, illegal deforestation rates for agricultural expansion and mining appear to be increasing as enforcement agencies scale back deployment due to Covid-19.6 These trends are generally driven by reduced risk of crimes being detected due to restricted access to the countryside, combined with reduced enforcement activity, but in some cases will also be driven by the collapse of alternative, more environmentally friendly livelihoods. As economies attempt to recover from the pandemic's financial impact, there is an increasing risk that environmental and climate regulations will be pushed back. Some individuals and organizations are already taking the opportunity to weaken these regulations while public attention is directed elsewhere, providing a foretaste of this potential future. The US Environmental Protection Agency, for example, issued a sweeping suspension of its enforcement of environmental laws on 26 March, telling companies they would not need to meet the full set of environmental standards during the coronavirus outbreak.7 Similarly, Indonesia has ceased the certification scheme for legal timber, reportedly in response to falling timber exports, stoking fears of an illegal logging boom.8 Some of these trends, especially spikes in bushmeat trade and deforestation, will increase the risk of future zoonotic disease outbreaks (Cunningham et al., 2017; Faust et al., 2018). The first reaction of many governments and institutions to Covid-19 has been to reduce the risk of spreading the infection. Social distancing and non-essential travel/work rules have meant that many (albeit not all) field trips and field seasons have been cancelled or postponed. In addition to the direct impacts on many research projects and postgraduate research students, this means that many species recovery programmes and site restoration initiatives are unable to continue critical work. The New Zealand Government, for example, has halted invasive predator trap checks on public land.9 Monitoring programmes of wildlife populations, communities and ecosystems are also not taking place. This will have long-term consequences for our understanding of biodiversity dynamics through the loss of data sequences that have run for decades. This is especially distressing because we are missing the opportunity to capture biodiversity responses to relaxation of anthropogenic pressures arising from Covid-19, such as reduced road traffic, air pollution, and numbers of people visiting the countryside and protected areas. Now is the time to step up development and investment in new ways of observing nature remotely, for example through advances in robotics or acoustic recording systems, that do not rely on researchers being present on the ground to gather data. This is the only way to ensure that future pandemics do not puncture long-term monitoring datasets. A second issue is that many community-based conservation projects in countries with low gross national income per capita are reliant on the input, financial and otherwise, from individuals and institutions from elsewhere within those countries and more affluent ones. With travel restrictions reducing engagement with these communities, there is the potential for community conservation and education projects to fold, or local communities to seek other livelihoods including from wildlife exploitation. As universities cancel summer field schools and study abroad programmes wholesale, significant financial support is also lost to field research centres and local communities. Finally, ecotourism is a major revenue earner for many communities. The lack of travel and the impact that this has on tourism infrastructure, think closure of airlines and air routes, will remove that source of livelihoods for these individuals and communities. There is a significant risk that they, and others facing increasing poverty, will turn to alternative means of monetizing the wildlife that lives in these regions. Their ability to do so will be facilitated by reduced investment of ecotourism funds in the enforcement of natural resource management rules. There is still considerable debate about the precise origins of Covid-19, although it is likely to have originated in bats and transmitted to humans via another species, potentially pangolins (Zhang et al., 2020). Domestic animal, livestock and wildlife markets have long been identified as a major risk factor in promoting zoonotic outbreaks and are linked to the emergence of human infections by other viral pathogens including other coronaviruses and HIV type viruses (e.g. Woo et al., 2006; van Heuverswyn & Peeters, 2007). While there is the potential for a public vendetta against species perceived to be the cause of the virus (Kingston, 2016), there is also an unprecedented opportunity to educate the public about the risks associated with the consumption of wildlife, and apply pressure to close down, or at least better regulate, wildlife markets. Some such Asian markets have already been temporarily shut, reducing the legal and illegal trade in wildlife species, but zoonotic disease emergence from wildlife trade and consumption could arise on any continent. While there is a risk of a resurgent black-market in wildlife trade, as happened when a ban was attempted in 2003 in response to the SARS outbreak,10 there are clear opportunities for reforming wildlife trade and easing pressure on wild populations. There is a more general notable opportunity for public education on the links between key pressures on biodiversity, such as deforestation and wildlife trade and consumption, and the risk of pandemics (Cunningham et al., 2017; Faust et al., 2018; MacDonald & Mordecai, 2019). Such education programmes combined with people's personal experience of the severe adverse impacts of pandemics could be a powerful force in lobbying decision-makers to enhance environmental protection. Over one-third of the globe's human population is currently in some form of lock-down, with daily activities curtailed to their home or close surroundings. With limited sources of entertainment available, many of these people are finding solace in observing wildlife around their home and returning to home-based activities including gardening. There is great potential here for conservationists to help people through the lock-down by strengthening these bonds with nature, through garden-based citizen science programmes and encouraging more wildlife friendly management of gardens. Given increased urbanization and the inappropriate management of much urban greenspace, such changes are a key requirement for maximizing urban biodiversity (Aronson et al., 2017). More generally, forging new long-term bonds with nature is a perquisite for maintaining future donations to conservation funders (helping to meet the first challenge) and generating public pressure on politicians to include conservation objectives when planning the post-Covid-19 world (helping to meet the first and second challenges). There are other ways in which humanity can do things differently post-Covid-19. The social distancing measures that currently accompany lock-downs are predicted to remain in place in some form for many months – sufficiently long for new habits to form. Dare we hope that familiarity with home working, remote meetings and networking will deliver a new normal of significantly reduced commuting to work and international business travel, including by conservation biologists that enable some of the current marked falls in air pollution and greenhouse gas emissions to be maintained? Humanity has demonstrated its resilience to global shocks, including bouncing back from two world wars and previous pandemics including the Great Plague and the 'Spanish' flu. Unfortunately, we also demonstrate failure to learn from our past mistakes under the false impressions that everything will be fine. The conservation community, like much of the world, seems to have been caught off-guard by the rapidly unfolding and escalating impacts of Covid-19. We must minimize the negative impacts as much as possible while remaining vigilant to identify and counter anti-environmental efforts, take advantage of the opportunities to enhance conservation, and ensure that the conservation community is better prepared for the next pandemic. The authors would like to thank Julie K. Young for her valuable comments and input on this editorial. The opinions expressed in this editorial are those of the authors and do not necessarily represent the views of their employers or affiliated institutions.