Received: 29 March 2020 | Accepted: 1 June 2020 DOI: 10.1111/gcb.15272

PRIMARY RESEARCH ARTICLE

Ecological forecasts to inform near-term management of threats to

Ayesha I. T. Tulloch1 | Valerie Hagger2 | Aaron C. Greenville1

1School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, Abstract Australia are being altered by rapid and interacting changes in natural processes and 2 School of Biological Sciences, The anthropogenic threats to biodiversity. Uncertainty in historical, current and future ef- University of Queensland, St. Lucia, Qld, Australia fectiveness of actions hampers decisions about how to mitigate changes to prevent bio- diversity loss and species extinctions. Research in management, agriculture and Correspondence Ayesha I. T. Tulloch, Desert health indicates that forecasts predicting the effects of near-term or seasonal environ- Research Group, Heydon-Laurence Building mental conditions on management greatly improve outcomes. Such forecasts help re- A08, School of Life and Environmental Sciences, University of Sydney, Sydney, solve uncertainties about when and how to operationalize management. We reviewed NSW 2006, Australia. the scientific literature on environmental management to investigate whether near-term Email: [email protected] forecasts are developed to inform biodiversity decisions in Australia, a nation with one of Funding information the highest recent extinction rates across the globe. We found that forecasts focused on Australian Research Council, Grant/Award Number: DE170100599; Ecological Society economic objectives (e.g. ) predict on significantly shorter timelines of Australia; Commonwealth Scientific and and answer a broader range of management questions than forecasts focused on biodi- Industrial Research Organisation versity conservation. We then evaluated scientific literature on the effectiveness of 484 actions to manage seven major terrestrial threats in Australia, to identify opportunities for near-term forecasts to inform operational conservation decisions. Depending on the action, between 30% and 80% threat management operations experienced near-term impacts on outcomes before, during or after management. Disease control, spe- cies translocation/reintroduction and restoration actions were most frequently impacted, and negative impacts such as increased species mortality and reduced recruit- ment were more likely than positive impacts. Drought or dry conditions, and rainfall, were the most frequently reported weather impacts, indicating that near-term forecasts pre- dicting the effects of low or excessive rainfall on management outcomes are likely to have the greatest benefits. Across the world, many regions are, like Australia, becoming warmer and drier, or experiencing more extreme rainfall events. Informing conservation decisions with near-term and seasonal ecological forecasting will be critical to harness uncertainties and lower the risk of threat management failure under global change.

KEYWORDS change, conservation decision-making, ecological prediction models, fire management, habitat restoration, , near-term ecological forecasting, species reintroduction, species translocation, threatening processes

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd

.wileyonlinelibrary.com/journal/gcb  Glob Change Biol. 2020;26:5816–5828 | 5816 TULLOCH et al. | 5817

1 | INTRODUCTION (Dietze et al., 2018; Hobbs, Higgs, & Harris, 2009; Hobday, 2011; Maron, Rhodes, & Gibbons, 2013). Anthropogenic threats, such as habitat loss, resource exploita- Ecological predictions range in their precision from ‘hunches’ tion, invasive organisms and interactions among these drivers, to conceptual and qualitative models based on practitioner learn- have led to catastrophic recent declines in the numbers and sizes ings and experience (Tulloch, Possingham, & Wilson, 2011) to of of species across the globe (Ceballos, Ehrlich, & complex quantitative models based on long-term empirical data Dirzo, 2017). Rapid changes in natural processes have also oc- (White et al., 2019). Many formal predictions are conducted once curred during this time, leading to disrupted climate and fire re- and never updated or assessed (Dietze et al., 2018), despite this gimes that can interact synergistically with anthropogenic threats, being best practice (Hobday et al., 2019). For most of earth's sys- enhancing negative impacts (Geary, Nimmo, Doherty, Ritchie, & tems that are undergoing rapid change, such ‘once-off’ models Tulloch, 2019). Urgent decisions are needed about where, when are neither realistic nor are they practical, as they will become and how to allocate scarce conservation resources to mitigate outdated and provide inaccurate recommendations when system threats and recover populations. However, uncertainty in the conditions change. The increasing recognition that more fre- likely effectiveness of threat management actions slows conser- quent, extensive and intense extreme weather events and their vation decisions and decreases the effectiveness of actions. This associated events (e.g. wildfires, coral bleaching) are impacting can result in species extinctions if actions are delayed too long biodiversity persistence makes accurate, useable forecasts even (Martin et al., 2012). In the fields of marine fisheries and agricul- more critical (Maxwell et al., 2019). Without regular updates to tural production, managers face similar issues of global change such forecasts, conservation management decisions will lack the and future uncertainty, and have begun tackling such issues using most current data, and the longer a prediction remains out of near-term and seasonal forecasting models (Hobday, Spillman, date, the less accurate it becomes (Dietze et al., 2018; Petchey Eveson, & Hartog, 2016; Klopper, Vogel, & Landman, 2006; et al., 2015). Meinke & Stone, 2005; Spillman, Alves, Hudson, Hobday, & How can the scientific provide the best available Hartog, 2011). Seasonal forecasts predict environmental condi- scientific predictions of what will happen in the future to managers tions at daily, weekly and monthly intervals up to a year into the and policy makers? Ecological forecasting is the ‘process of predict- future (e.g. Spillman & Alves, 2009). By providing more defini- ing the state of ecosystems, services and natural capi- tive and accurate short-term yield or stock predictions (Brown, tal’ under future scenarios such as for climate, land use and human Hochman, Holzworth, & Horan, 2018), these forecasts enable , with clearly specified uncertainties (Clark et al., 2001). more effective operationalization of decisions and increase the It aims to answer questions about the future condition of ecosys- economic benefits of resource management choices (Gunda, tems as well as their likely responses to actions. Until recently, Bazuin, Nay, & Yeung, 2017; NOAA, 2016). Applying such fore- the bulk of model-based ecological forecasts were scenario-based casts to predicting the future state of ecological systems should projections focused on responses on multidecadal have similar benefits for management and conservation of terres- timescales. Forecast timelines were driven by underlying climate trial ecosystems (Clark et al., 2001; Dietze, 2017). Despite this, projections and their scenarios, such as the Intergovernmental Panel the extent to which near-term forecasting is currently integrated on Climate Change 2100 decadal projections (IPCC, 2014), which into terrestrial conservation research is unknown. Here, we con- were first based on Global Circulation Models, and later included duct a review of research informing management of threats to Representative Concentration Pathways to provide additional biodiversity to determine whether forecasts are potentially useful needed climate-model inputs such as emissions, concentrations and for informing near-term operational conservation decisions, and land use/cover (Moss et al., 2010). However, recent studies have in which contexts near-term ecological forecasting might improve pointed to a potential mismatch between such long planning time- conservation management success. lines and the timescales of environmental decision-making, which For both conservation managers and policy makers, success in tend to require near-term (daily to decadal) data-initialized pre- dealing with environmental change rests with a capacity to antici- dictions, as well as projections that evaluate decision alternatives pate and predict the future state of a system or study species (Clark (Hobday et al., 2016). Near-term forecasts solve this problem by pro- et al., 2001). Many previous attempts to predict how to manage and viding the opportunity to iteratively cycle between performing anal- prevent biodiversity declines relied solely on our understanding of yses and updating predictions in light of new evidence, depending on the past, for example, by setting historical baselines in ecological the forecast model used (Dietze et al., 2018). This iterative process condition then striving to reach these baselines through one or more of gaining feedback, building experience and correcting models and ecosystem recovery actions. This is problematic because our sys- methods is critical for improving forecasts (Tetlock & Gardner, 2015). tems are rapidly moving outside known historical or natural variabil- Management responses can be evaluated and implemented ahead of ity into a new Anthropocene era (Pecl et al., 2017). Whilst the past time to reduce impacts that result from unfavourable conditions and tells us about the historical state and responses to change of popu- maximize opportunities when optimal conditions occur. Monitoring lations, it may no longer be the most appropriate benchmark for the of management outcomes enables decision-makers to iteratively up- management of ecological communities today and into the future date the next forecast cycle. 5818 | TULLOCH et al.

Whilst an increasing number of examples of iterative and time-bound prediction of the future state of the system or species. near-term ecological forecasts now exist in a range of fields Of the 281 articles, 182 were excluded due to not meeting at least from fisheries management (Hobday et al., 2016), to crop de- one of these criteria. signs (Rodriguez et al., 2018), to managing human disease spread We reviewed the remaining 99 articles to determine key char- (Shaman & Karspeck, 2012), there has been limited effort to acteristics of each study and how these characteristics related to track the different ecological forecasts in use today (but see the timescale of prediction. To link study objectives to the types Payne et al., 2017 for an examination of some marine forecast of forecast used, we first characterized: (a) study system: terres- products). Aside from one recent study (Hagger, Dwyer, Shoo, & trial, marine or freshwater; (b) broad management target: resource Wilson, 2018), there have also been no efforts to independently production/management (e.g. agriculture, fisheries, forestry) or assess how near-term forecasts might improve conservation de- biodiversity conservation (populations of species, ecological com- cisions. To rectify this gap in knowledge, we explore whether munities or ecosystems), (c) broad planning timeline: long-term near-term and seasonal forecasts are being produced by the con- ‘strategic’ planning (>10 years into the future) or shorter term ‘op- servation literature compared with the resource management erational’ planning (≤10 years into the future) and (d) objectives of and agricultural production literature across marine, freshwater management: where to allocate resources, how to allocate manage- and terrestrial ecosystems. We evaluate whether the iterative ment (e.g. which actions to do), when to do management or which nature and shorter timelines of marine and terrestrial near-term components of biodiversity to manage. A study could be character- forecasts enable a greater breadth of operational questions ized as more than one of each criterion if appropriate (e.g. targeting to be answered. We focus on Australia as a case study, as the both when and how to allocate management). We then determined Australian continent holds one of the worst contemporary and the timescale for each article's forecast in terms of the lead time to rapid (<200 years) extinction records and continues to grap- the first prediction (taken as the first prediction year for the fore- ple with how to effectively respond (McDonald et al., 2015). cast minus the study's publication year, such that if a paper was Australia's threatened species continue to decline from multi- published in 2000, and made its first forecast for the year 2100, ple threats, including widespread land clearing and habitat loss then the lead time is 100 years), the number of time periods pre- driven largely by agricultural and urban development, invasive dicted (i.e. number of forecasts) and the overall time period for all species such as cats, foxes, weeds and feral , dis- forecasts. ease and altered fire regimes (Doherty, Glen, Nimmo, Ritchie, & For marine, freshwater and terrestrial systems, we first built Dickman, 2016; Kearney et al., 2019; Reside et al., 2017; Ward contingency tables quantifying the frequency of broad management et al., 2019). We review the scientific literature on the historical target (production or conservation) and planning goal (long term or effectiveness of management actions to mitigate these threats near term), and calculated Fisher's exact test statistics for each study across the terrestrial bioregions of Australia. We determine system due to small sample sizes to test whether the planning time- which threat mitigation actions might benefit from near-term line was independent of the management goal at p = .05 significance forecasts that reduce uncertainty in seasonal conditions. Finally, level. There were not enough freshwater studies to compute this we evaluate what gaps exist in our ability to predict management statistic. We then combined all the ecosystems and built three gen- outcomes successfully. eralized linear regression models (GLMs) in R version 3.5.1 (R Core Team, 2019) to ask whether the planning goals of a study influenced either the lead time to an article's forecast (i.e. the minimum time 2 | MATERIALS AND METHODS to the first prediction in months in years, a Gaussian-distributed dependent variable), the maximum length of the article's forecast The first goal of this review was to determine what types of fore- in years (a Gaussian-distributed dependent variable) or the number casts are produced by literature focused on managing threats to of forecasts produced (a Poisson-distributed dependent variable biodiversity (conservation-focused), compared with literature representing the number of different timelines the study produced focused on managing biodiversity as a natural resource, for ex- forecasts at). Planning goals were represented by two categorical ample, fisheries and forestry or agriculture (production-focused). variables, planning goal and broad management target. Each of the We reviewed peer-reviewed literature (Web of Science, accessed three models included an interaction between the two categorical 7 November 2018) to create a database of articles with the fol- predictors to explore whether planning goals affected the forecast lowing search terms: (forecast* OR predict* OR project*) AND characteristics independently or together. We also used generalized future AND (climate change OR global change OR anthropogenic linear regression to evaluate the relationship between the lead time change OR weather OR threat) AND (species OR ecosystem*) to an article's forecast and the total number of forecasts produced AND: (manage* OR protect* OR restore*) AND Australia*. Criteria by an article. for inclusion in our review were: (a) focused on Australia's ter- To ask whether near-term forecasts enable a greater breadth restrial, marine or freshwater natural resources; (b) considered of management questions to be answered, we used ordinal lo- a management or planning action; (c) considered objectives be- gistic regression in R (package MASS version 5.1.4, Venables & yond economic development or health; and (d) made at least one Ripley, 2002). We built a single variate model with independent TULLOCH et al. | 5819 variable representing the timescale of an article's forecast, that evaluated effects of weather on management at each location. is, whether a forecast focused on shorter (<10 years) or longer Similarly, if an action was carried out over more than one time pe- term (i.e. >10 years) outcomes, and the breadth of management riod, we reported weather effects in each time period separately. objectives addressed as dependent variable (categorical with pos- This resulted in a final data set of 484 ‘action-by-site-by-time- sible values of 1, 2, 3 or 4, representing how many of the follow- period’ combinations for review. ing objectives were addressed: ‘when’, ‘how’, ‘where’ and ‘which species’). The second goal of the review was to explore reporting of 3 | RESULTS weather impacts on terrestrial management actions to discover opportunities for near-term forecasting to inform operational 3.1 | Availability of ecological forecasts to manage management decisions. We evaluated the historical effective- Australian environments ness of eight management actions to mitigate the most destruc- tive threats to biodiversity in Australia (Kearney et al., 2019). The types of forecasts in the scientific literature to inform manage- These actions were: habitat restoration, weed control, livestock ment decisions differed depending on whether they were focused management (typically grazing exclusion and destocking), man- on long-term ‘strategic’ versus near-term ‘operational’ planning, aging feral herbivores, managing invasive predators, disease henceforth long term and near term respectively (for details, see control, managing fire regimes and species translocations or Appendix S1). Planning timelines (near term vs. long term) and man- reintroductions. We conducted a second review of the peer- agement goals (conservation vs. production) were not independ- reviewed environmental and ecological management literature to ent (Fisher's exact test, p = .0002). Conservation studies typically determine, for each action, which management outcomes have been impacted by near-term or seasonal issues (e.g. drought, cold/hot temperatures, rainfall). We created an initial database of articles by searching for the following terms in Web of Science (accessed 14 November 2018): (impact* OR effect* OR response* OR respond*) AND (conservation OR biodiversity) AND (species OR ecosystem*) AND Australia*. Within this database, we then searched for terms specific to each of the eight management actions (e.g. for weed control, additional terms of ‘weed*’ AND (management OR control* OR eradicat*) were used to siphon out articles related to eradicating, controlling or managing weeds in Australia (see Appendix S1 for full list of search terms for each management action and number of articles reviewed). Criteria for inclusion in our review were: (a) focused on Australia's eco- systems or species; and (b) evaluating the outcomes of a man- agement action to restore or maintain biodiversity. Due to high overlap between weed control articles and habitat restoration articles, we decided to combine these two categories, result- ing in seven threat management actions and 241 articles for evaluation. For each study, we collected the following data: specific action and ecosystem being targeted for management, type of weather impact (dryness/drought, high temperatures, cold temperatures/ frost, flooding, strong winds, cyclones, ), the timing of the im- pact (before, during or after management action was implemented), the direction of the impact (positive, negative or uncertain) and how management outcomes were affected (partial or complete failure). FIGURE 1 Distribution of forecasts for biodiversity We also collected data on the location of management actions (spa- conservation versus resource management and production, tial context—latitude and longitude if available, spatial scale of man- (a) across freshwater (n = 11), marine (n = 21) and terrestrial agement, bioregion), timing of management (temporal context—year (n = 67) study systems, and (b) across four forecast objectives of how, where or when to do management. Both graphs indicate management commenced, year management ended, year of man- the relative proportion of each system or goal targeted towards agement evaluation), other non-weather-related operational issues broad planning goals of either long-term (>10 years into the affecting management outcomes. If a study reported management future) strategic planning or near-term (<10 years into the future) in more than one independent (i.e. spatially disjunct) location, we operational management 5820 | TULLOCH et al. focused on long planning timescales, whereas production articles production-focused and operational studies that developed fore- were more evenly spread across near- and long-term planning casts with significantly shorter lead times (Figure S2 and Table (Figure 1a). S3 in Supplementary Material). There was also a significant inter- The planning timeline and the management goal independently action between planning timelines and management goals, with influenced a study's forecast timelines, with conservation- studies that were focused on operations and production having focused studies and strategic planning studies having signifi- significantly shorter maximum forecast times than other produc- cantly longer lead times to the first forecast in comparison with tion and conservation studies (Table S3). Operational forecasts

FIGURE 2 Near-term weather impacts on the effectiveness of seven management actions in studies for biodiversity conservation in Australia, showing (a) the percentage of management studies impacted by weather versus non-weather issues, (b) the percentage of weather-impacted studies that were impacted positively (where near-term weather effects improved outcomes) versus negatively (where near-term weather effects worsened outcomes), (c) the percentage of weather- impacted studies that were affected either before, during or after management actions took place and (d) the percentage of actions that were either negatively or positively impacted by each of nine weather effects. Note that for (a), some studies reported both weather and non- weather effects, so percentages do not add up to 100 TULLOCH et al. | 5821

FIGURE 3 Frequency of six near-term weather issues impacting the outcomes of seven management actions to mitigate threats to biodiversity in Australia

also produced significantly more forecast outputs—on aver- 3.2 | Near-term weather impacts on terrestrial age, 31 (±29.50 SE) forecasts per study for conservation and management actions 22 (±8.35 SE) forecasts per study for production studies, com- pared with strategic long-term forecasts which produced 4.27 Half (49.8%) of 484 threat management actions across Australia (±1.46 SE) and 3.27 (±0.82 SE) forecasts per study for conser- were impacted by at least one near-term weather issue. The prev- vation and production goals respectively. There was a significant alence of weather impacts on management effectiveness varied positive relationship between the lead time to first forecast and across actions. Disease control, species translocation/reintroduc- the number of forecast outputs, with long-term studies producing tion and habitat restoration (including weed control) were most significantly fewer forecasts (i.e. a fewer number of timelines fore- frequently impacted by weather issues (62%, 84% and 59% of ac- cast) compared with near-term studies (GLM: β = −0.197 ± 0.058, tions, respectively, Figure 2a). Feral control actions were 2 p = .001, R = .11). least frequently impacted by weather (65% of actions did not report For terrestrial systems, planning timescales were independent weather impacts, Figure 2a). For most actions, weather was more of management goals (Fisher's exact test, p = .216), whereas there likely to have a negative impact than a positive impact on outcomes was a significant interaction between the planning timeframe and (Figure 2b). Weather impacts were most prevalent during manage- management goal (production or conservation) for marine system ment implementation compared with before or after actions were studies (Fisher's exact test, p = .007). Whilst a third of marine stud- completed (mean percentage of studies impacted before, during or ies developed forecasts targeted at near-term decisions (mostly for after: 27.4 ± 3.7 SE, 53.5 ± 3.7 SE and 27.6 ± 6.2 SE, respectively; fisheries goals), only 7% of terrestrial (predominantly for production Figure 2c). Impacts were more likely to be negative for most weather goals), and no freshwater studies developed near-term forecasts, fo- effects except for flooding and high rainfall (Figure 2d). cusing instead on long-term planning for biodiversity conservation Negative weather impacts on management occurred due to a va- (Figure 1a). riety of reasons. For example, unforeseen dry conditions prevented The breadth of management questions (when, where, how, which disease control actions such as fungicides from effectively penetrat- species) differed between long- and near-term forecasts. Near-term ing phytophthora management sites (Dunstan et al., 2010), and re- forecasts addressed significantly more management questions than duced the survival and of translocated and reintroduced long-term forecasts (2.7 ± 0.31 SE and 1.34 ± 0.06 SE questions per species such as threatened eastern barred bandicoots (Perameles study, respectively; ordinal logistic regression: β = −0.012 ± 0.008, gunnii; Todd, Jenkins, & Bearlin, 2002) and black-footed rock-wal- p < .001). Regardless of whether they targeted biodiversity conser- labies (Petrogale lateralis; West, Ward, Foster, & Taggart, 2017). vation or resource management, forecasts focused on near-term Although negative impacts were more common for most manage- goals answered more questions about when to allocate resources ment actions (Figure 2b), positive impacts of weather were more fre- (33% of conservation- and 100% of production-focused near-term quent for grazing management, and were most often associated with forecasts) compared with long-term forecasts, which mostly answer higher than average rainfall in eastern temperate and subtropical the question of where to allocate management (78% of conservation- bioregions that resulted in increased plant and animal recruitment and 68% of production-focused long-term forecasts; Figure 1b). and in destocked sites (Edwards, Croft, & Dawson, 1996; However, where to allocate resources was still an important question Frank, Wardle, Dickman, & Greenville, 2014; Kutt, Vanderduys, & for near-term forecasts (67% of conservation and 89% of production O'Reagain, 2012; Page & Beeton, 2000; Zimmer, Mavromihalis, focused). Turner, Moxham, & Liu, 2010; Figure 2d). 5822 | TULLOCH et al.

The most frequently reported weather impacts were drought or impacts for translocations/reintroductions, fire management, dis- dry conditions and high rainfall (28% and 25% of all studies respec- ease control and habitat restoration (Figure 3). Rainfall was the most tively). Dry conditions and drought were the most common weather frequently reported weather factor for grazing management, feral

FIGURE 4 Reviewed management studies in different biogeographical (IBRA) regions of Australia, showing (a) the proportion of reviewed studies that were impacted by weather, (b) the proportion of studies where weather negatively affected management outcomes, (c) the proportion of studies where weather negatively affected management outcomes, (d) the proportion of all studies impacted by rainfall and (e) the proportion of all studies impacted by dry conditions or drought. Dots indicate approximate locations of 257 reported study sites TULLOCH et al. | 5823 herbivore control and invasive predator control (Figure 3). The least increases in the frequency of extreme heat events and decreases in reported weather impacts were cyclones/strong winds and flooding, rainfall over the past decade (Bureau of Meteorology & CSIRO, 2018). each affecting only 3% of all studies (Figure 3). Climate fluctuations are inherently unpredictable beyond a few years Weather impacts were spread across the entire continent, to a decade (Branstator & Teng, 2010). Although long-term projec- with studies in Central and Western Australia's arid and semi-arid tions of management success will be hugely uncertain, near-term rangelands more frequently impacted than other parts of Australia forecasts have higher accuracy due to their ability to be iteratively (Figure 4a). Only 33% of regions with studies reported more positive updated to constantly improve models over time through validation than negative impacts of weather (Figure 4b), with positive impacts and performance testing (Hudson, Shi, et al., 2017). Our study shows mostly confined to the eastern temperate and subtropical bioregions that predicting weather impacts in the short term can be equally or (most often due to high rainfall assisting with recolonization or sur- more important to the success or failure of biodiversity management vival of managed biodiversity; Appendix S2). Negative impacts were actions compared with long-term and non-weather impacts such as more frequent in the western arid bioregions, southern alpine, mon- climate change, legacy effects of historical management and insuf- tane and semiarid bioregions, northern savannah and south-west ficient effort and intensity (Figure 2; Figure S4). The management biodiversity hotspot (Figure 4c). Weather impacted all published actions we reviewed are not confined to Australia, as threats such as management studies in 10 bioregions (6% of IBRA regions). Rainfall disease, changed fire regimes, livestock grazing and habitat degrada- and drought impacted actions in 33 (55%) and 39 (66%) of bioregions tion are global drivers of biodiversity declines. respectively (Figure 4d,e). Whilst our review successfully revealed that weather impacts in the near term are important to the success or failure of biodiversity management actions, it did have some limitations. Because research 4 | DISCUSSION does not always begin as applied, our focus on only scientific ar- ticles might have underestimated the lead times of forecasts that The demand for forecasts that predict how ecosystems might respond may have started off as strategic but later have been operationalized to intervention is increasing in land and sea applications, driven by an after uptake from end-user organizations. Furthermore, most stud- international shift in environmental change research from detection ies did not explicitly link weather to outcomes, instead discussing to mitigation and adaptation (Dietze et al., 2018; Payne et al., 2017). the reasons for management success or failure. Failure to explicitly Near-term forecasts are already in use in many production-focused report effect sizes is a common problem in meta-analyses (Geary, disciplines (Figure 1; Payne et al., 2017), as well as in public health, Doherty, Nimmo, Tulloch, & Ritchie, 2020). If effect sizes are not human disease and medical applications (Lowe et al., 2017; Thomson evaluated, it is difficult to know the full scale of the weather impact, et al., 2006). However, our review shows that biodiversity conserva- information that is needed by land managers to make decisions on tion research still focuses on long-term forecasts of up to 100 years trade-offs. For example, if effects are small, is delaying due to fore- into the future to inform decisions, a timeline set by the limits of casted ‘bad weather’ worse than losing a year in undertaking the global long-term climate models (IPCC, 2014) rather than manage- conservation action? ment needs. Biodiversity conservation needs to anticipate and adapt An additional challenge in any meta-analysis is that non-signif- to climate changes, building ecological predictions that target man- icant results and ‘failed’ management experiments (e.g. activities agement in a way that spends limited resources effectively whilst that were unsuccessful due to climate anomalies) are less likely to accounting for avoidable impacts such as near-term weather issues. be published in primary literature and therefore would have been Such impacts cannot be ignored—near-term weather impacts such as less likely to be included in this review (Koricheva, Gurevitch, & drought and rainfall have affected biodiversity management across Mengersen, 2013). Because of this, our study possibly underesti- 60% of the Australian continent's terrestrial bioregions over the past mates forecasting potential for many regions and actions. We found four decades (Figure 4). For some terrestrial management decisions, few management studies in central and northern Australia, perhaps weather affected up to 80% of reported actions (Figure 2), and more because of a lack of reporting and evaluation, or due to the remote- impacts were negative than positive. Failed management could lead ness and difficulty in undertaking research and management in some to economic and biodiversity losses that might have been prevented regions. More studies were from south-eastern and south-western or mitigated if near-term forecasts using readily available weather Australia (Figure 4a), which comprise some of Australia's greatest data had been built (Hagger et al., 2018). population pressures and biodiversity hotspots, with many species Predicting the likely success or failure of management makes and ecosystems likely to benefit from efforts to forecast the effec- sense not just from an economic perspective—it is crucial to adap- tiveness of conservation outcomes. The challenges and outcomes tation in an age of rapid environmental change. According to a range of biodiversity management in better studied Australian regions, of modelled scenarios, many arid, semiarid and temperate regions and the effect of weather, may not be applicable to other regions across the world are likely to become warmer and drier over the next and parts of the world where there are few or no management 20 years due to human-induced climate change, whilst many tropical studies. Furthermore, in regions with more predictable monsoonal regions could experience more extreme and damaging rainfall events weather patterns, such as in tropical northern Australia, manag- (IPCC, 2014). For example, southern Australia has experienced ers might be better prepared for seasonal impacts of weather on 5824 | TULLOCH et al. biodiversity conservation actions and therefore more knowledge- European organizations (Bruno Soares & Dessai, 2016). Australia's able about when and where to act to avoid negative impacts of conservation scientists might lack confidence in climate predic- weather. However, even ‘predictable’ monsoonal regions have fu- tions themselves, or in how to incorporate their data with climate ture uncertainty in the frequency and intensity of extreme weather forecasts; alternatively, they might lack capacity, time or funding to events such as cyclones and flooding (Knutson et al., 2010), and develop and interpret complex models prior to action; or see low many locations are likely to experience increased temperatures and applicability to some contexts (Bruno Soares & Dessai, 2016; Hagger intensified droughts (Dai, 2011). Such changed and variable weather et al., 2018; Tulloch et al., 2016). Close, long-term interactions with patterns may affect the success of numerous timing-critical man- climate forecast providers and organizational expertise and capacity agement actions including fire management (due to increased fire are critical enablers to the use of seasonal climate forecasting (Bruno risk under drought situations, Herawati & Santoso, 2011) and habi- Soares & Dessai, 2016). Increasing uptake would require validation tat restoration (Chazdon, 2003). Priority should be given to synthe- of forecast skill for the regions, seasons, variables and lead times of sizing existing knowledge on the impacts of short-term weather on interest, and quantifying the ecological and financial benefits that management outcomes, through additional channels such as expert can be provided, recognizing that forecasts are probabilistic and will workshops, eliciting anecdotal information from practitioners and be wrong in some years. Developing user-friendly web-based fore- traditional owners and grey literature review. casting tools (e.g. R Shiny web apps) that require minimal technical Near-term forecasting requires time series or repeated mea- expertise and provide information on how to interpret uncertainty surement data (Dietze et al., 2018). Data are required on the study would enhance the application of near-term forecasting to real man- system or species and on environmental conditions relevant to the agement problems (see Hazen et al., 2018 for an example of such a dynamics of the system (e.g. weather). To evaluate management tool for marine forecasting). success, we also require a measure of how the management action Australia's unique environment and changing climate lead to dis- may affect the system or species, gained either from historical con- tinct opportunities and challenges for near-term forecasting. The trol-impact studies or expert estimation (e.g. for novel actions). The Australian Community Climate Earth-System Simulator-Seasonal data must be at a time and spatial scale similar to the ecological pro- (ACCESS-S1) is the new version of Australia's seasonal prediction cesses of interest and all data must be interoperable, that is, able to system. At a resolution of 60 km, ACCESS-S1 predicts Australian work together within the modelling framework (Dietze et al., 2018). climate drivers, including the El Niño Southern Oscillation and With the growth of open data sets, many relevant data sets for fore- the Indian Ocean Dipole, across the different regional of casting are now available. However, the lack of a nationally funded Australia's Great Dividing Range and the eastern seaboard. It of- strategic long-term population monitoring programme in Australia fers improved forecast skill for rainfall, maximum temperature and (Lindenmayer, 2017) has undermined Australia's ability to accu- minimum temperature on multi-week timescales compared to the mulate the data needed for near-term forecasting. Such long-term previous seasonal prediction system (Predictive Ocean Atmosphere monitoring is vital to measure the success of management actions Model for Australia, POAMA; Hudson, Alves, et al., 2017). However, and iteratively update models to better direct resources, particularly the forecast skill of ACCESS-S1 varies with region, time of year, vari- after unprecedented events, such as the 2019–2020 able and forecast lead time, and is reduced in places with high in- wildfire season (Nolan et al., 2020). Despite most terrestrial manage- terannual variability (e.g. much of Australia's arid zone). Forecasts ment actions being at risk of weather-related disturbance impacting for temperature are generally more accurate than rainfall, and over- success to some degree (Figures 2 and 3), most do not prepare for all skill decreases as lead time increases (Hudson, Shi, et al., 2017). such risk through developing near-term forecasts (Figure 1). Near-term weather forecasting models built in other regions of the We suspect that the lack of near-term forecasts in terrestrial bio- world include the North American Multi-Model Ensemble (NMME; diversity conservation is due not only to a lack of long-term ecolog- Kirtman et al., 2014), which provides free publicly available hindcast ical data but also to inadequate reporting of management outcomes and real-time prediction data, and the United Kingdom's Met Office and data on critical climate drivers such as rainfall. Aspects of rain- global seasonal forecast system (GloSea5; MacLachlan et al., 2015), fall such as spatial heterogeneity, intra-annual rainfall variability and whose products are currently only available to licensed users. Each seasonality have been identified as key drivers of diversity, structure, model has costs and benefits in terms of spatial and temporal resolu- function and natural selection in terrestrial ecosystems (Engelbrecht tion, forecast skill and accessibility (Barnston, Tippett, Ranganathan, et al., 2007; Knapp et al., 2002; Siepielski et al., 2017). Inaccurate & L'Heureux, 2019; MacLachlan et al., 2015; Shukla et al., 2019). rainfall data and mapping lead to high variability and uncertainty in Although near-term ecological forecasts appear to be limited in predictions (McCain & Colwell, 2011), which could lead to reduced terrestrial and research, especially in con- user confidence in near-term climate forecasts for terrestrial sys- servation applications, in marine ecosystems, they are increasingly tems compared with marine systems that are driven by variables commonplace (Figure 1). In fact, until recently most biodiversity-fo- that are more accurately predicted, such as temperature (Doney cused near-term and seasonal forecasts in Australia (and the globe) et al., 2012). Indeed, the low reliability and skill of seasonal climate came from fisheries and marine conservation (Payne et al., 2017). forecasts in certain environments was identified as one of the most Their progress in this area came from a combination of massive ad- important reasons for the lack of seasonal ecological forecasting by vances in global climatic and oceanographic mapping, a wealth of TULLOCH et al. | 5825 observational data from historical and current monitoring of fish previously considered separately to manage risks due to short-term stocks (Eveson, Hobday, Hartog, Spillman, & Rough, 2015) that al- environmental variability and long-term change (Hobday et al., 2018). lows relationships between the environment and the species to be Near-term forecasting is likely to play an increasing role in planning, well characterized, a study system with a long ‘memory’ where the scheduling and delivery of conservation actions that rely on suitable variables that modulate species' abundances and distributions (e.g. weather conditions. By learning from advances in other disciplines, temperature) are predicted directly by climate forecast systems engaging meaningfully with on-ground management and identifying (Payne et al., 2017), strong relationships between fisheries and cli- opportunities where the benefits of producing forecasts outweigh mate researchers and an economic incentive to maximize fisheries costs, biodiversity conservation scientists can close the gap between production outcomes. In fisheries and aquaculture operations, sea- the potential and the reality of near-term ecological forecasting. sonal forecasting is used to reduce uncertainty and manage business risks, including developing species-specific habitat forecasts for ACKNOWLEDGEMENTS Southern Bluefin Tuna in the Australian Bight to improve operational A.I.T.T. was funded by an Australian Research Council Discovery planning of fishers (Eveson et al., 2015; Hobday et al., 2016). In ma- Early Career Researcher Award and the Wiley Next Generation rine conservation, seasonal forecasts of sea surface temperatures Ecologist Award from the Ecological Society of Australia. V.H. was inform Great Barrier Reef management actions for predicted coral funded by a Commonwealth Scientific and Industrial Research bleaching events (Smith & Spillman, 2019). By collaborating with and Organisation (CSIRO) scholarship. learning from advances in the marine research community, research- ers in terrestrial and freshwater environments can harness import- DATA AVAILABILITY STATEMENT ant skills and experience without the need to start from scratch. The data that support the findings of this study are openly available For near-term ecological forecasting to be useful, there must be in figshare at https://doi.org/10.6084/m9.figsh​are.12616787. management decisions that can be adapted to avoid or mitigate the impacts, at lead times that match the skill of the seasonal forecast ORCID (Hobday et al., 2018). For those conservation actions where man- Ayesha I. T. Tulloch https://orcid.org/0000-0002-5866-1923 agement failed due to weather prior to implementation (e.g. fire Valerie Hagger https://orcid.org/0000-0001-8408-8449 management and invasive predator control), the decision lead time Aaron C. Greenville https://orcid.org/0000-0002-0113-4778 required may be too long, and the forecast skill during the critical en- vironmental period for adapting decisions may decline. For example, REFERENCES drought prior to baiting of invasive predators can increase the ef- Allsop, S. E., Dundas, S. J., Adams, P. J., Kreplins, T. L., Bateman, P. 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